The purpose of this project is to predict the final rankings in the 2024 World Figure Skating Championships for the Men’s and Women’s Single categories. Figure skating is an Olympic sport where the athlete performs individually, in pairs, or in groups on ice on figure skates. The Olympic disciplines are men’s single, women’s singles, pair skating, and ice dance, and those are also split within various levels from beginner to senior elite. For this project, we will focus on the senior elite level for men’s and women’s singles, since they will compete at the World Championships and have the same scoring styles, unlike pair skating and ice dance.
Figure skating competitions at the elite level are held over 2 days where skaters are expected to perform 2 programs, the short program and the free skate (also referred to as the free program), and earn scores for both programs. Their final score is based on the sum of the 2 scores.
Typically, fans of the sport might predict the rankings of the World Championships based on a skater’s top score or average score of the current season. However, the problem with that is that each event has different sets of judges who may have different consistencies in scoring. A perfect skate at one event can be judged to be full of mistakes at another. Presentation and artistry are also evaluated, which could be subjective. The location of the event could also have an effect on the performance of a skater, like higher elevation could cause poorer performances. Therefore, the average scores of a competition could vary a lot and a winning score at one event could receive a lower score at another.
So, I wanted to see how the variation between competition scores could skew our ideas of the scores during the World Championships!
All scores from International Skating Union recognized competitions are posted on their website. However it can be messy and have varying formats, so I used SkatingScores, which has the scores in clean, organized tables.
Within a regular figure skating season, there are 9 major ISU events leading up to Worlds: 6 Grand Prix events, a Grand Prix Final where the top 6 skaters during the 6 GP events get to compete, the European Championships, and the 4 Continents Championships. During a Winter Olympic year, the Olympics serve as an extra competition.
No skater will compete at all 9 events. 4 Continents is a competition for nations not part of Europe, so no skater will be competing in both the 4 Continents and European Championships. Grand Prix events are by invite only and skaters can only attend 2 at most. At most, a skater can attend 4 of these competitions or 5 for an Olympic year. At the least but rarely, a skater could not compete at any. So there could be potential missing data there, which we will look into.
I am using data from the 2016-2017, 2017-18, 2018-19, 2021-22, and 2022-23 competitive seasons. I skipped 2019-20 because the World Championships were cancelled due to COVID-19. I also skipped 2020-21 because a lot of events were cancelled also due to COVID, giving me insufficient data. The 2021-22 also did not have a Grand Prix Final event because of COVID, but otherwise there is a sufficient amount of data from that season.
Here is the regular score dataset, where I merged all of the scores from the 9/10 regular competitions over the mentioned seasons. I also added columns specifying the sex of the skater and the competition and the year of the score. I selected the year based on what year the corresponding World Championship competition occurs.
#merge regular scores
reg_merged <- dplyr::bind_rows(list(score23, score22, score19, score18, score17), .id = 'year')
#add year column
reg_merged <- reg_merged %>%
clean_names() %>%
mutate(year = ifelse(year == 1, 2023,
ifelse(year == 2, 2022,
ifelse(year == 3, 2019,
ifelse(year == 4, 2018, 2017)))))
#clean skaters
reg_merged$skater <- gsub('[0-9]', '', reg_merged$skater)
reg_merged$skater <- str_squish(reg_merged$skater)
reg_merged %>%
kable() %>%
kable_styling(full_width = F) %>%
scroll_box(width = "100%", height = "200px")
| year | final_rank | skater | nation | sp_score | sp_rank | fs_score | fs_rank | total_score | competition | sex |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023 | 1 | Kaori Sakamoto | JP | 71.72 | 1 | 145.89 | 1 | 217.61 | GPUSA | Female |
| 2023 | 2 | Isabeau Levito | US | 71.30 | 2 | 135.36 | 2 | 206.66 | GPUSA | Female |
| 2023 | 3 | Amber Glenn | US | 68.42 | 3 | 129.19 | 3 | 197.61 | GPUSA | Female |
| 2023 | 4 | Haein Lee | KR | 66.24 | 4 | 113.26 | 5 | 179.50 | GPUSA | Female |
| 2023 | 5 | Ekaterina Kurakova | PL | 63.65 | 6 | 115.03 | 4 | 178.68 | GPUSA | Female |
| 2023 | 6 | Gracie Gold | US | 64.18 | 5 | 109.91 | 6 | 174.09 | GPUSA | Female |
| 2023 | 7 | Nicole Schott | DE | 56.47 | 10 | 103.88 | 8 | 160.35 | GPUSA | Female |
| 2023 | 8 | Yeonjeong Park | KR | 60.04 | 7 | 98.54 | 9 | 158.58 | GPUSA | Female |
| 2023 | 9 | Ahsun Yun | KR | 47.98 | 11 | 108.72 | 7 | 156.70 | GPUSA | Female |
| 2023 | 10 | Eliska Brezinova | CZ | 56.65 | 9 | 96.92 | 10 | 153.57 | GPUSA | Female |
| 2023 | 11 | Marilena Kitromilis | CY | 46.01 | 12 | 89.47 | 11 | 135.48 | GPUSA | Female |
| 2023 | NA | Rino Matsuike | JP | 59.50 | 8 | NA | NA | NA | GPUSA | Female |
| 2023 | 1 | Ilia Malinin | US | 86.08 | 4 | 194.29 | 1 | 280.37 | GPUSA | Male |
| 2023 | 2 | Kao Miura | JP | 94.96 | 1 | 178.23 | 2 | 273.19 | GPUSA | Male |
| 2023 | 3 | Junhwan Cha | KR | 94.44 | 2 | 169.61 | 3 | 264.05 | GPUSA | Male |
| 2023 | 4 | Daniel Grassl | IT | 88.43 | 3 | 169.25 | 4 | 257.68 | GPUSA | Male |
| 2023 | 5 | Roman Sadovsky | CA | 78.15 | 5 | 147.26 | 7 | 225.41 | GPUSA | Male |
| 2023 | 6 | Wesley Chiu | CA | 71.58 | 9 | 148.32 | 6 | 219.90 | GPUSA | Male |
| 2023 | 7 | Liam Kapeikis | US | 74.29 | 8 | 145.21 | 8 | 219.50 | GPUSA | Male |
| 2023 | 8 | Sena Miyake | JP | 77.87 | 6 | 137.87 | 9 | 215.74 | GPUSA | Male |
| 2023 | 9 | Koshiro Shimada | JP | 62.54 | 12 | 152.58 | 5 | 215.12 | GPUSA | Male |
| 2023 | 10 | Dinh Tran | US | 64.99 | 11 | 134.69 | 10 | 199.68 | GPUSA | Male |
| 2023 | 11 | Mihhail Selevko | EE | 75.75 | 7 | 116.05 | 12 | 191.80 | GPUSA | Male |
| 2023 | 12 | Donovan Carrillo | MX | 69.18 | 10 | 119.10 | 11 | 188.28 | GPUSA | Male |
| 2023 | 1 | Yelim Kim | KR | 72.22 | 1 | 132.27 | 2 | 204.49 | GPJPN | Female |
| 2023 | 2 | Kaori Sakamoto | JP | 68.07 | 2 | 133.80 | 1 | 201.87 | GPJPN | Female |
| 2023 | 3 | Rion Sumiyoshi | JP | 68.01 | 3 | 125.11 | 4 | 193.12 | GPJPN | Female |
| 2023 | 4 | Audrey Shin | US | 65.87 | 4 | 123.13 | 5 | 189.00 | GPJPN | Female |
| 2023 | 5 | Rinka Watanabe | JP | 58.36 | 9 | 129.71 | 3 | 188.07 | GPJPN | Female |
| 2023 | 6 | Seoyeon Ji | KR | 62.92 | 6 | 121.22 | 7 | 184.14 | GPJPN | Female |
| 2023 | 7 | Niina Petrõkina | EE | 58.81 | 8 | 121.48 | 6 | 180.29 | GPJPN | Female |
| 2023 | 8 | Seoyeong Wi | KR | 61.06 | 7 | 115.68 | 10 | 176.74 | GPJPN | Female |
| 2023 | 9 | Starr Andrews | US | 64.13 | 5 | 109.93 | 12 | 174.06 | GPJPN | Female |
| 2023 | 10 | Olga Mikutina | AT | 56.95 | 10 | 116.41 | 9 | 173.36 | GPJPN | Female |
| 2023 | 11 | Amber Glenn | US | 52.04 | 11 | 117.32 | 8 | 169.36 | GPJPN | Female |
| 2023 | 12 | Eva Lotta Kiibus | EE | 48.56 | 12 | 113.81 | 11 | 162.37 | GPJPN | Female |
| 2023 | 1 | Shoma Uno | JP | 91.66 | 2 | 188.10 | 1 | 279.76 | GPJPN | Male |
| 2023 | 2 | Sota Yamamoto | JP | 96.49 | 1 | 161.36 | 6 | 257.85 | GPJPN | Male |
| 2023 | 3 | Junhwan Cha | KR | 80.35 | 6 | 174.41 | 2 | 254.76 | GPJPN | Male |
| 2023 | 4 | Kazuki Tomono | JP | 85.07 | 4 | 166.76 | 3 | 251.83 | GPJPN | Male |
| 2023 | 5 | Adam Siao Him Fa | FR | 87.44 | 3 | 163.01 | 4 | 250.45 | GPJPN | Male |
| 2023 | 6 | Matteo Rizzo | IT | 78.57 | 7 | 162.19 | 5 | 240.76 | GPJPN | Male |
| 2023 | 7 | Nika Egadze | GE | 84.47 | 5 | 148.39 | 8 | 232.86 | GPJPN | Male |
| 2023 | 8 | Stephen Gogolev | CA | 69.01 | 9 | 152.01 | 7 | 221.02 | GPJPN | Male |
| 2023 | 9 | Gabriele Frangipani | IT | 68.78 | 10 | 143.53 | 9 | 212.31 | GPJPN | Male |
| 2023 | 10 | Conrad Orzel | CA | 73.10 | 8 | 129.59 | 11 | 202.69 | GPJPN | Male |
| 2023 | 11 | Maurizio Zandrón | AT | 68.21 | 11 | 133.51 | 10 | 201.72 | GPJPN | Male |
| 2023 | 12 | Tomoki Hiwatashi | US | 57.18 | 12 | 127.87 | 12 | 185.05 | GPJPN | Male |
| 2023 | 1 | Mai Mihara | JP | 72.23 | 1 | 145.20 | 1 | 217.43 | GPGBR | Female |
| 2023 | 2 | Isabeau Levito | US | 72.06 | 2 | 143.68 | 2 | 215.74 | GPGBR | Female |
| 2023 | 3 | Anastasiia Gubanova | GE | 66.82 | 3 | 126.29 | 5 | 193.11 | GPGBR | Female |
| 2023 | 4 | Young You | KR | 61.21 | 6 | 130.15 | 3 | 191.36 | GPGBR | Female |
| 2023 | 5 | Ekaterina Kurakova | PL | 63.46 | 4 | 126.98 | 4 | 190.44 | GPGBR | Female |
| 2023 | 6 | Nicole Schott | DE | 60.38 | 7 | 121.03 | 6 | 181.41 | GPGBR | Female |
| 2023 | 7 | Gabriella Izzo | US | 62.92 | 5 | 111.18 | 7 | 174.10 | GPGBR | Female |
| 2023 | 8 | Gabrielle Daleman | CA | 58.95 | 8 | 104.82 | 8 | 163.77 | GPGBR | Female |
| 2023 | 9 | Alexia Paganini | CH | 54.63 | 11 | 102.26 | 10 | 156.89 | GPGBR | Female |
| 2023 | 10 | Julia Sauter | RO | 52.38 | 12 | 104.08 | 9 | 156.46 | GPGBR | Female |
| 2023 | 11 | Natasha Mckay | GB | 57.62 | 9 | 97.58 | 11 | 155.20 | GPGBR | Female |
| 2023 | 12 | Bradie Tennell | US | 56.50 | 10 | 96.69 | 12 | 153.19 | GPGBR | Female |
| 2023 | 1 | Daniel Grassl | IT | 86.85 | 2 | 177.50 | 1 | 264.35 | GPGBR | Male |
| 2023 | 2 | Deniss Vasiljevs | LV | 83.01 | 3 | 171.55 | 2 | 254.56 | GPGBR | Male |
| 2023 | 3 | Shun Sato | JP | 82.68 | 4 | 166.35 | 3 | 249.03 | GPGBR | Male |
| 2023 | 4 | Koshiro Shimada | JP | 80.84 | 5 | 166.33 | 4 | 247.17 | GPGBR | Male |
| 2023 | 5 | Tatsuya Tsuboi | JP | 76.75 | 7 | 149.38 | 5 | 226.13 | GPGBR | Male |
| 2023 | 6 | Roman Sadovsky | CA | 89.49 | 1 | 129.86 | 8 | 219.35 | GPGBR | Male |
| 2023 | 7 | Jimmy Ma | US | 77.72 | 6 | 136.75 | 7 | 214.47 | GPGBR | Male |
| 2023 | 8 | Morisi Kvitelashvili | GE | 56.42 | 12 | 138.83 | 6 | 195.25 | GPGBR | Male |
| 2023 | 9 | Tomoki Hiwatashi | US | 66.68 | 8 | 122.05 | 9 | 188.73 | GPGBR | Male |
| 2023 | 10 | Corey Circelli | CA | 62.97 | 10 | 119.84 | 10 | 182.81 | GPGBR | Male |
| 2023 | 11 | Graham Newberry | GB | 64.30 | 9 | 116.12 | 12 | 180.42 | GPGBR | Male |
| 2023 | 12 | Edward Appleby | GB | 62.52 | 11 | 117.61 | 11 | 180.13 | GPGBR | Male |
| 2023 | 1 | Loena Hendrickx | BE | 72.75 | 1 | 143.59 | 1 | 216.34 | GPFRA | Female |
| 2023 | 2 | Yelim Kim | KR | 68.93 | 2 | 125.83 | 4 | 194.76 | GPFRA | Female |
| 2023 | 3 | Rion Sumiyoshi | JP | 64.10 | 5 | 130.24 | 3 | 194.34 | GPFRA | Female |
| 2023 | 4 | Haein Lee | KR | 62.77 | 6 | 130.72 | 2 | 193.49 | GPFRA | Female |
| 2023 | 5 | Audrey Shin | US | 64.27 | 4 | 119.66 | 5 | 183.93 | GPFRA | Female |
| 2023 | 6 | Mana Kawabe | JP | 68.83 | 3 | 113.67 | 8 | 182.50 | GPFRA | Female |
| 2023 | 7 | Rino Matsuike | JP | 57.68 | 9 | 118.84 | 6 | 176.52 | GPFRA | Female |
| 2023 | 8 | Maé-Bérénice Méité | FR | 58.84 | 8 | 116.84 | 7 | 175.68 | GPFRA | Female |
| 2023 | 9 | Léa Serna | FR | 62.63 | 7 | 105.26 | 9 | 167.89 | GPFRA | Female |
| 2023 | 10 | Olga Mikutina | AT | 56.00 | 10 | 103.99 | 10 | 159.99 | GPFRA | Female |
| 2023 | 11 | Lindsay Van Zundert | NL | 55.11 | 11 | 98.98 | 11 | 154.09 | GPFRA | Female |
| 2023 | 12 | Maïa Mazzara | FR | 46.05 | 12 | 94.80 | 12 | 140.85 | GPFRA | Female |
| 2023 | 1 | Adam Siao Him Fa | FR | 88.00 | 3 | 180.98 | 1 | 268.98 | GPFRA | Male |
| 2023 | 2 | Sota Yamamoto | JP | 92.42 | 1 | 165.48 | 3 | 257.90 | GPFRA | Male |
| 2023 | 3 | Kazuki Tomono | JP | 89.46 | 2 | 159.31 | 4 | 248.77 | GPFRA | Male |
| 2023 | 4 | Sihyeong Lee | KR | 76.54 | 7 | 166.08 | 2 | 242.62 | GPFRA | Male |
| 2023 | 5 | Nika Egadze | GE | 82.44 | 4 | 150.96 | 6 | 233.40 | GPFRA | Male |
| 2023 | 6 | Luc Economides | FR | 77.23 | 6 | 152.41 | 5 | 229.64 | GPFRA | Male |
| 2023 | 7 | Lukas Britschgi | CH | 74.25 | 9 | 148.61 | 7 | 222.86 | GPFRA | Male |
| 2023 | 8 | Ivan Shmuratko | UA | 75.95 | 8 | 144.13 | 8 | 220.08 | GPFRA | Male |
| 2023 | 9 | Mihhail Selevko | EE | 79.40 | 5 | 133.52 | 11 | 212.92 | GPFRA | Male |
| 2023 | 10 | Wesley Chiu | CA | 67.95 | 11 | 142.00 | 10 | 209.95 | GPFRA | Male |
| 2023 | 11 | Landry Le May | FR | 60.87 | 12 | 142.52 | 9 | 203.39 | GPFRA | Male |
| 2023 | NA | Sena Miyake | JP | 69.27 | 10 | NA | NA | NA | GPFRA | Male |
| 2023 | 1 | Mai Mihara | JP | 73.58 | 2 | 130.56 | 1 | 204.14 | GPFIN | Female |
| 2023 | 2 | Loena Hendrickx | BE | 74.88 | 1 | 129.03 | 3 | 203.91 | GPFIN | Female |
| 2023 | 3 | Mana Kawabe | JP | 67.03 | 3 | 130.38 | 2 | 197.41 | GPFIN | Female |
| 2023 | 4 | Rika Kihira | JP | 64.07 | 6 | 128.36 | 4 | 192.43 | GPFIN | Female |
| 2023 | 5 | Madeline Schizas | CA | 65.19 | 5 | 122.65 | 5 | 187.84 | GPFIN | Female |
| 2023 | 6 | Lindsay Thorngren | US | 65.75 | 4 | 117.48 | 6 | 183.23 | GPFIN | Female |
| 2023 | 7 | Anastasiia Gubanova | GE | 56.03 | 9 | 110.54 | 8 | 166.57 | GPFIN | Female |
| 2023 | 8 | Bradie Tennell | US | 60.64 | 7 | 103.34 | 9 | 163.98 | GPFIN | Female |
| 2023 | 9 | Jenni Saarinen | FI | 59.69 | 8 | 95.95 | 11 | 155.64 | GPFIN | Female |
| 2023 | 10 | Janna Jyrkinen | FI | 42.89 | 12 | 111.56 | 7 | 154.45 | GPFIN | Female |
| 2023 | 11 | Linnea Ceder | FI | 55.63 | 10 | 96.28 | 10 | 151.91 | GPFIN | Female |
| 2023 | 12 | Eva Lotta Kiibus | EE | 49.27 | 11 | 89.62 | 12 | 138.89 | GPFIN | Female |
| 2023 | 1 | Ilia Malinin | US | 85.57 | 2 | 192.82 | 1 | 278.39 | GPFIN | Male |
| 2023 | 2 | Shun Sato | JP | 81.59 | 3 | 180.62 | 2 | 262.21 | GPFIN | Male |
| 2023 | 3 | Kévin Aymoz | FR | 88.96 | 1 | 166.73 | 3 | 255.69 | GPFIN | Male |
| 2023 | 4 | Tatsuya Tsuboi | JP | 78.82 | 5 | 166.08 | 4 | 244.90 | GPFIN | Male |
| 2023 | 5 | Camden Pulkinen | US | 72.45 | 7 | 157.47 | 5 | 229.92 | GPFIN | Male |
| 2023 | 6 | Nikolaj Majorov | SE | 69.94 | 8 | 139.61 | 6 | 209.55 | GPFIN | Male |
| 2023 | 7 | Arlet Levandi | EE | 72.67 | 6 | 136.83 | 7 | 209.50 | GPFIN | Male |
| 2023 | 8 | Keegan Messing | CA | 80.12 | 4 | 124.90 | 12 | 205.02 | GPFIN | Male |
| 2023 | 9 | Valtter Virtanen | FI | 69.15 | 9 | 134.87 | 8 | 204.02 | GPFIN | Male |
| 2023 | 10 | Aleksandr Selevko | EE | 66.96 | 11 | 132.51 | 10 | 199.47 | GPFIN | Male |
| 2023 | 11 | Lucas Tsuyoshi Honda | JP | 67.92 | 10 | 129.98 | 11 | 197.90 | GPFIN | Male |
| 2023 | 12 | Morisi Kvitelashvili | GE | 62.42 | 12 | 134.38 | 9 | 196.80 | GPFIN | Male |
| 2023 | 1 | Mai Mihara | JP | 74.58 | 2 | 133.59 | 1 | 208.17 | GPF | Female |
| 2023 | 2 | Isabeau Levito | US | 69.26 | 5 | 127.97 | 2 | 197.23 | GPF | Female |
| 2023 | 3 | Loena Hendrickx | BE | 74.24 | 3 | 122.11 | 4 | 196.35 | GPF | Female |
| 2023 | 4 | Rinka Watanabe | JP | 72.58 | 4 | 123.43 | 3 | 196.01 | GPF | Female |
| 2023 | 5 | Kaori Sakamoto | JP | 75.86 | 1 | 116.70 | 6 | 192.56 | GPF | Female |
| 2023 | 6 | Yelim Kim | KR | 61.55 | 6 | 119.03 | 5 | 180.58 | GPF | Female |
| 2023 | 1 | Shoma Uno | JP | 99.99 | 1 | 204.47 | 1 | 304.46 | GPF | Male |
| 2023 | 2 | Sota Yamamoto | JP | 94.86 | 2 | 179.49 | 3 | 274.35 | GPF | Male |
| 2023 | 3 | Ilia Malinin | US | 80.10 | 5 | 191.84 | 2 | 271.94 | GPF | Male |
| 2023 | 4 | Shun Sato | JP | 76.62 | 6 | 173.54 | 4 | 250.16 | GPF | Male |
| 2023 | 5 | Kao Miura | JP | 87.07 | 3 | 158.67 | 6 | 245.74 | GPF | Male |
| 2023 | 6 | Daniel Grassl | IT | 80.40 | 4 | 164.57 | 5 | 244.97 | GPF | Male |
| 2023 | 1 | Rinka Watanabe | JP | 63.27 | 6 | 134.32 | 1 | 197.59 | GPCAN | Female |
| 2023 | 2 | Starr Andrews | US | 64.69 | 5 | 126.57 | 2 | 191.26 | GPCAN | Female |
| 2023 | 3 | Young You | KR | 65.10 | 4 | 125.05 | 4 | 190.15 | GPCAN | Female |
| 2023 | 4 | Ava Marie Ziegler | US | 66.49 | 3 | 120.27 | 7 | 186.76 | GPCAN | Female |
| 2023 | 5 | Rika Kihira | JP | 59.27 | 8 | 125.06 | 3 | 184.33 | GPCAN | Female |
| 2023 | 6 | Niina Petrõkina | EE | 61.68 | 7 | 119.66 | 8 | 181.34 | GPCAN | Female |
| 2023 | 7 | Madeline Schizas | CA | 67.90 | 1 | 112.69 | 9 | 180.59 | GPCAN | Female |
| 2023 | 8 | Yuhana Yokoi | JP | 54.87 | 12 | 123.86 | 5 | 178.73 | GPCAN | Female |
| 2023 | 9 | Lindsay Thorngren | US | 55.16 | 10 | 120.93 | 6 | 176.09 | GPCAN | Female |
| 2023 | 10 | Gabrielle Daleman | CA | 66.65 | 2 | 104.96 | 11 | 171.61 | GPCAN | Female |
| 2023 | 11 | Lindsay Van Zundert | NL | 55.22 | 9 | 105.74 | 10 | 160.96 | GPCAN | Female |
| 2023 | 12 | Eliska Brezinova | CZ | 55.14 | 11 | 103.89 | 12 | 159.03 | GPCAN | Female |
| 2023 | 1 | Shoma Uno | JP | 89.98 | 2 | 183.17 | 1 | 273.15 | GPCAN | Male |
| 2023 | 2 | Kao Miura | JP | 94.06 | 1 | 171.23 | 2 | 265.29 | GPCAN | Male |
| 2023 | 3 | Matteo Rizzo | IT | 81.18 | 3 | 169.85 | 4 | 251.03 | GPCAN | Male |
| 2023 | 4 | Keegan Messing | CA | 79.69 | 4 | 171.03 | 3 | 250.72 | GPCAN | Male |
| 2023 | 5 | Camden Pulkinen | US | 75.07 | 5 | 143.99 | 8 | 219.06 | GPCAN | Male |
| 2023 | 6 | Lukas Britschgi | CH | 64.35 | 8 | 148.08 | 6 | 212.43 | GPCAN | Male |
| 2023 | 7 | Stephen Gogolev | CA | 57.94 | 11 | 152.70 | 5 | 210.64 | GPCAN | Male |
| 2023 | 8 | Aleksandr Selevko | EE | 60.37 | 10 | 145.74 | 7 | 206.11 | GPCAN | Male |
| 2023 | 9 | Jimmy Ma | US | 61.73 | 9 | 142.66 | 9 | 204.39 | GPCAN | Male |
| 2023 | 10 | Deniss Vasiljevs | LV | 69.01 | 7 | 128.44 | 10 | 197.45 | GPCAN | Male |
| 2023 | 11 | Conrad Orzel | CA | 69.69 | 6 | 125.73 | 11 | 195.42 | GPCAN | Male |
| 2023 | 1 | Anastasiia Gubanova | GE | 69.81 | 1 | 130.10 | 1 | 199.91 | EC | Female |
| 2023 | 2 | Loena Hendrickx | BE | 67.85 | 2 | 125.63 | 3 | 193.48 | EC | Female |
| 2023 | 3 | Kimmy Repond | CH | 63.83 | 3 | 128.68 | 2 | 192.51 | EC | Female |
| 2023 | 4 | Ekaterina Kurakova | PL | 61.81 | 5 | 125.09 | 4 | 186.90 | EC | Female |
| 2023 | 5 | Nina Pinzarrone | BE | 61.35 | 6 | 124.57 | 5 | 185.92 | EC | Female |
| 2023 | 6 | Niina Petrõkina | EE | 61.05 | 7 | 122.69 | 6 | 183.74 | EC | Female |
| 2023 | 7 | Janna Jyrkinen | FI | 60.77 | 8 | 116.19 | 7 | 176.96 | EC | Female |
| 2023 | 8 | Lara Naki Gutmann | IT | 55.39 | 13 | 113.90 | 8 | 169.29 | EC | Female |
| 2023 | 9 | Nicole Schott | DE | 54.33 | 16 | 109.49 | 9 | 163.82 | EC | Female |
| 2023 | 10 | Julia Sauter | RO | 56.58 | 11 | 103.84 | 12 | 160.42 | EC | Female |
| 2023 | 11 | Sofja Stepchenko | LV | 55.32 | 14 | 104.02 | 11 | 159.34 | EC | Female |
| 2023 | 12 | Olga Mikutina | AT | 62.78 | 4 | 96.30 | 18 | 159.08 | EC | Female |
| 2023 | 13 | Marilena Kitromilis | CY | 53.71 | 18 | 105.20 | 10 | 158.91 | EC | Female |
| 2023 | 14 | Lindsay Van Zundert | NL | 58.13 | 10 | 99.97 | 15 | 158.10 | EC | Female |
| 2023 | 15 | Eva Lotta Kiibus | EE | 55.26 | 15 | 101.69 | 13 | 156.95 | EC | Female |
| 2023 | 16 | Alexandra Feigin | BG | 54.31 | 17 | 100.92 | 14 | 155.23 | EC | Female |
| 2023 | 17 | Josefin Taljegård | SE | 55.53 | 12 | 99.45 | 16 | 154.98 | EC | Female |
| 2023 | 18 | Livia Kaiser | CH | 60.25 | 9 | 90.95 | 20 | 151.20 | EC | Female |
| 2023 | 19 | Natasha Mckay | GB | 51.94 | 20 | 96.06 | 19 | 148.00 | EC | Female |
| 2023 | 20 | Anastasia Gozhva | UA | 46.78 | 22 | 96.91 | 17 | 143.69 | EC | Female |
| 2023 | 21 | Daša Grm | SI | 52.47 | 19 | 90.58 | 21 | 143.05 | EC | Female |
| 2023 | 22 | Mia Caroline Risa Gomez | NO | 49.14 | 21 | 88.48 | 22 | 137.62 | EC | Female |
| 2023 | 23 | Júlia Láng | HU | 46.33 | 23 | 83.95 | 23 | 130.28 | EC | Female |
| 2023 | 24 | Nikola Rychtaříková | CZ | 45.64 | 24 | 77.49 | 24 | 123.13 | EC | Female |
| 2023 | 25 | Alexandra Michaela Filcová | SK | 43.94 | 25 | NA | NA | NA | EC | Female |
| 2023 | 26 | Léa Serna | FR | 43.93 | 26 | NA | NA | NA | EC | Female |
| 2023 | 27 | Antonina Dubinina | RS | 42.51 | 27 | NA | NA | NA | EC | Female |
| 2023 | 28 | Anastasia Gracheva | MD | 39.08 | 28 | NA | NA | NA | EC | Female |
| 2023 | 29 | Alexandra Mintsidou | GR | 33.86 | 29 | NA | NA | NA | EC | Female |
| 2023 | 1 | Adam Siao Him Fa | FR | 96.53 | 1 | 171.24 | 2 | 267.77 | EC | Male |
| 2023 | 2 | Matteo Rizzo | IT | 86.46 | 2 | 173.46 | 1 | 259.92 | EC | Male |
| 2023 | 3 | Lukas Britschgi | CH | 79.26 | 5 | 168.75 | 3 | 248.01 | EC | Male |
| 2023 | 4 | Kévin Aymoz | FR | 83.75 | 4 | 157.17 | 4 | 240.92 | EC | Male |
| 2023 | 5 | Deniss Vasiljevs | LV | 84.81 | 3 | 151.54 | 6 | 236.35 | EC | Male |
| 2023 | 6 | Daniel Grassl | IT | 77.03 | 8 | 153.80 | 5 | 230.83 | EC | Male |
| 2023 | 7 | Nika Egadze | GE | 72.96 | 12 | 147.69 | 7 | 220.65 | EC | Male |
| 2023 | 8 | Mihhail Selevko | EE | 73.74 | 11 | 144.56 | 8 | 218.30 | EC | Male |
| 2023 | 9 | Andreas Nordebäck | SE | 75.98 | 9 | 136.97 | 10 | 212.95 | EC | Male |
| 2023 | 10 | Gabriele Frangipani | IT | 77.35 | 7 | 134.27 | 12 | 211.62 | EC | Male |
| 2023 | 11 | Maurizio Zandrón | AT | 72.57 | 13 | 135.11 | 11 | 207.68 | EC | Male |
| 2023 | 12 | Tomás Guarino Sabaté | ES | 71.65 | 14 | 133.54 | 13 | 205.19 | EC | Male |
| 2023 | 13 | Mark Gorodnitsky | IL | 64.94 | 22 | 137.40 | 9 | 202.34 | EC | Male |
| 2023 | 14 | Valtter Virtanen | FI | 68.33 | 18 | 129.95 | 14 | 198.28 | EC | Male |
| 2023 | 15 | Nikita Starostin | DE | 74.70 | 10 | 123.27 | 17 | 197.97 | EC | Male |
| 2023 | 16 | Morisi Kvitelashvili | GE | 70.55 | 16 | 124.04 | 16 | 194.59 | EC | Male |
| 2023 | 17 | Vladimir Samoilov | PL | 78.26 | 6 | 113.33 | 21 | 191.59 | EC | Male |
| 2023 | 18 | Adam Hagara | SK | 65.15 | 21 | 124.57 | 15 | 189.72 | EC | Male |
| 2023 | 19 | Jari Kessler | HR | 67.87 | 19 | 114.96 | 19 | 182.83 | EC | Male |
| 2023 | 20 | Burak Demirboga | TR | 64.33 | 23 | 118.49 | 18 | 182.82 | EC | Male |
| 2023 | 21 | Kyrylo Marsak | UA | 70.41 | 17 | 111.57 | 22 | 181.98 | EC | Male |
| 2023 | 22 | Davidé Lewton Brain | MC | 66.07 | 20 | 113.47 | 20 | 179.54 | EC | Male |
| 2023 | 23 | Graham Newberry | GB | 70.85 | 15 | 103.79 | 24 | 174.64 | EC | Male |
| 2023 | 24 | Aleksandr Vlasenko | HU | 62.49 | 24 | 111.45 | 23 | 173.94 | EC | Male |
| 2023 | 25 | Petr Kotlarik | CZ | 60.24 | 25 | NA | NA | NA | EC | Male |
| 2023 | 26 | Georgiy Reshtenko | CZ | 54.52 | 26 | NA | NA | NA | EC | Male |
| 2023 | 27 | Larry Loupolover | BG | 53.26 | 27 | NA | NA | NA | EC | Male |
| 2023 | 28 | Samuel Mcallister | IE | 48.07 | 28 | NA | NA | NA | EC | Male |
| 2023 | 29 | David Sedej | SI | 46.28 | 29 | NA | NA | NA | EC | Male |
| 2023 | 1 | Haein Lee | KR | 69.13 | 6 | 141.71 | 1 | 210.84 | 4CC | Female |
| 2023 | 2 | Yelim Kim | KR | 72.84 | 1 | 136.45 | 3 | 209.29 | 4CC | Female |
| 2023 | 3 | Mone Chiba | JP | 67.28 | 7 | 137.70 | 2 | 204.98 | 4CC | Female |
| 2023 | 4 | Chaeyeon Kim | KR | 71.39 | 3 | 131.00 | 5 | 202.39 | 4CC | Female |
| 2023 | 5 | Rinka Watanabe | JP | 65.60 | 8 | 134.90 | 4 | 200.50 | 4CC | Female |
| 2023 | 6 | Bradie Tennell | US | 69.49 | 5 | 130.42 | 6 | 199.91 | 4CC | Female |
| 2023 | 7 | Amber Glenn | US | 69.63 | 4 | 122.87 | 8 | 192.50 | 4CC | Female |
| 2023 | 8 | Hana Yoshida | JP | 59.82 | 10 | 129.78 | 7 | 189.60 | 4CC | Female |
| 2023 | 9 | Sara-Maude Dupuis | CA | 51.68 | 12 | 118.99 | 9 | 170.67 | 4CC | Female |
| 2023 | 10 | Madeline Schizas | CA | 60.11 | 9 | 99.62 | 10 | 159.73 | 4CC | Female |
| 2023 | 11 | Justine Miclette | CA | 51.24 | 13 | 98.32 | 11 | 149.56 | 4CC | Female |
| 2023 | 12 | Jocelyn Hong | NZ | 52.02 | 11 | 96.31 | 12 | 148.33 | 4CC | Female |
| 2023 | 13 | Tzu-Han Ting | TW | 45.19 | 17 | 95.32 | 13 | 140.51 | 4CC | Female |
| 2023 | 14 | Tara Prasad | IN | 46.04 | 14 | 87.11 | 14 | 133.15 | 4CC | Female |
| 2023 | 15 | Joanna So | HK | 45.90 | 15 | 87.09 | 15 | 132.99 | 4CC | Female |
| 2023 | 16 | Anna Levkovets | KZ | 45.53 | 16 | 83.73 | 16 | 129.26 | 4CC | Female |
| 2023 | 17 | Sofia Farafonova | KZ | 44.66 | 18 | 81.82 | 17 | 126.48 | 4CC | Female |
| 2023 | 18 | Vlada Vasiliev | AU | 40.13 | 21 | 73.82 | 18 | 113.95 | 4CC | Female |
| 2023 | 19 | Cheuk Ka Kahlen Cheung | HK | 39.58 | 22 | 64.80 | 19 | 104.38 | 4CC | Female |
| 2023 | 20 | Hiu Yau Chow | HK | 42.10 | 20 | 58.06 | 21 | 100.16 | 4CC | Female |
| 2023 | 21 | Bagdana Rakhishova | KZ | 35.96 | 23 | 59.62 | 20 | 95.58 | 4CC | Female |
| 2023 | NA | Isabeau Levito | US | 71.50 | 2 | NA | NA | NA | 4CC | Female |
| 2023 | NA | Sofia Lexi Jacqueline Frank | PH | 43.82 | 19 | NA | NA | NA | 4CC | Female |
| 2023 | 1 | Kao Miura | JP | 91.90 | 1 | 189.63 | 1 | 281.53 | 4CC | Male |
| 2023 | 2 | Keegan Messing | CA | 86.70 | 2 | 188.87 | 2 | 275.57 | 4CC | Male |
| 2023 | 3 | Shun Sato | JP | 80.81 | 6 | 178.33 | 3 | 259.14 | 4CC | Male |
| 2023 | 4 | Junhwan Cha | KR | 83.77 | 5 | 166.37 | 4 | 250.14 | 4CC | Male |
| 2023 | 5 | Mikhail Shaidorov | KZ | 72.43 | 12 | 164.71 | 5 | 237.14 | 4CC | Male |
| 2023 | 6 | Sihyeong Lee | KR | 70.38 | 14 | 157.41 | 6 | 227.79 | 4CC | Male |
| 2023 | 7 | Boyang Jin | CN | 85.32 | 4 | 142.15 | 10 | 227.47 | 4CC | Male |
| 2023 | 8 | Conrad Orzel | CA | 80.09 | 7 | 146.01 | 7 | 226.10 | 4CC | Male |
| 2023 | 9 | Jimmy Ma | US | 86.64 | 3 | 134.40 | 13 | 221.04 | 4CC | Male |
| 2023 | 10 | Maxim Naumov | US | 75.96 | 8 | 142.75 | 9 | 218.71 | 4CC | Male |
| 2023 | 11 | Koshiro Shimada | JP | 74.06 | 10 | 143.79 | 8 | 217.85 | 4CC | Male |
| 2023 | 12 | Jaeseok Kyeong | KR | 75.30 | 9 | 136.68 | 12 | 211.98 | 4CC | Male |
| 2023 | 13 | Stephen Gogolev | CA | 72.82 | 11 | 136.94 | 11 | 209.76 | 4CC | Male |
| 2023 | 14 | Liam Kapeikis | US | 71.43 | 13 | 126.57 | 14 | 198.00 | 4CC | Male |
| 2023 | 15 | Yudong Chen | CN | 67.93 | 15 | 116.48 | 16 | 184.41 | 4CC | Male |
| 2023 | 16 | Dias Jirenbayev | KZ | 57.67 | 18 | 125.75 | 15 | 183.42 | 4CC | Male |
| 2023 | 17 | Edrian Paul Célestino | PH | 66.83 | 16 | 100.09 | 19 | 166.92 | 4CC | Male |
| 2023 | 18 | Darian Kaptich | AU | 58.22 | 17 | 107.91 | 17 | 166.13 | 4CC | Male |
| 2023 | 19 | Rakhat Bralin | KZ | 54.25 | 19 | 105.95 | 18 | 160.20 | 4CC | Male |
| 2023 | 20 | Pagiel Yie Ken Sng | SG | 48.52 | 20 | 90.31 | 20 | 138.83 | 4CC | Male |
| 2023 | 21 | Douglas Gerber | NZ | 40.75 | 21 | 85.20 | 21 | 125.95 | 4CC | Male |
| 2023 | 22 | Lap Kan Lincoln Yuen | HK | 36.33 | 22 | 80.71 | 22 | 117.04 | 4CC | Male |
| 2022 | 1 | Anna Shcherbakova | RU | 80.20 | 2 | 175.75 | 2 | 255.95 | OLY | Female |
| 2022 | 2 | Alexandra Trusova | RU | 74.60 | 4 | 177.13 | 1 | 251.73 | OLY | Female |
| 2022 | 3 | Kaori Sakamoto | JP | 79.84 | 3 | 153.29 | 3 | 233.13 | OLY | Female |
| 2022 | 4 | Kamila Valieva | RU | 82.16 | 1 | 141.93 | 5 | 224.09 | OLY | Female |
| 2022 | 5 | Wakaba Higuchi | JP | 73.51 | 5 | 140.93 | 6 | 214.44 | OLY | Female |
| 2022 | 6 | Young You | KR | 70.34 | 6 | 142.75 | 4 | 213.09 | OLY | Female |
| 2022 | 7 | Alysa Liu | US | 69.50 | 8 | 139.45 | 7 | 208.95 | OLY | Female |
| 2022 | 8 | Loena Hendrickx | BE | 70.09 | 7 | 136.70 | 9 | 206.79 | OLY | Female |
| 2022 | 9 | Yelim Kim | KR | 67.78 | 9 | 134.85 | 11 | 202.63 | OLY | Female |
| 2022 | 10 | Mariah Bell | US | 65.38 | 11 | 136.92 | 8 | 202.30 | OLY | Female |
| 2022 | 11 | Anastasiia Gubanova | GE | 65.40 | 10 | 135.58 | 10 | 200.98 | OLY | Female |
| 2022 | 12 | Ekaterina Kurakova | PL | 59.08 | 24 | 126.76 | 12 | 185.84 | OLY | Female |
| 2022 | 13 | Viktoriia Safonova | BY | 61.46 | 17 | 123.37 | 13 | 184.83 | OLY | Female |
| 2022 | 14 | Olga Mikutina | AT | 61.14 | 18 | 121.06 | 14 | 182.20 | OLY | Female |
| 2022 | 15 | Ekaterina Ryabova | AZ | 61.82 | 16 | 118.15 | 15 | 179.97 | OLY | Female |
| 2022 | 16 | Karen Chen | US | 64.11 | 13 | 115.82 | 17 | 179.93 | OLY | Female |
| 2022 | 17 | Nicole Schott | DE | 63.13 | 14 | 114.52 | 19 | 177.65 | OLY | Female |
| 2022 | 18 | Lindsay Van Zundert | NL | 59.24 | 22 | 116.57 | 16 | 175.81 | OLY | Female |
| 2022 | 19 | Madeline Schizas | CA | 60.53 | 20 | 115.03 | 18 | 175.56 | OLY | Female |
| 2022 | 20 | Eliska Brezinova | CZ | 64.31 | 12 | 111.10 | 21 | 175.41 | OLY | Female |
| 2022 | 21 | Eva Lotta Kiibus | EE | 59.55 | 21 | 112.20 | 20 | 171.75 | OLY | Female |
| 2022 | 22 | Alexia Paganini | CH | 61.06 | 19 | 107.85 | 22 | 168.91 | OLY | Female |
| 2022 | 23 | Mana Kawabe | JP | 62.69 | 15 | 104.04 | 23 | 166.73 | OLY | Female |
| 2022 | 24 | Alexandra Feigin | BG | 59.16 | 23 | 100.15 | 24 | 159.31 | OLY | Female |
| 2022 | 25 | Jenni Saarinen | FI | 56.97 | 25 | 96.07 | 25 | 153.04 | OLY | Female |
| 2022 | 26 | Josefin Taljegård | SE | 54.51 | 26 | NA | NA | NA | OLY | Female |
| 2022 | 27 | Yi Zhu | CN | 53.44 | 27 | NA | NA | NA | OLY | Female |
| 2022 | 28 | Natasha Mckay | GB | 52.54 | 28 | NA | NA | NA | OLY | Female |
| 2022 | 29 | Kailani Craine | AU | 49.93 | 29 | NA | NA | NA | OLY | Female |
| 2022 | 30 | Anastasiia Shabotova | UA | 48.68 | 30 | NA | NA | NA | OLY | Female |
| 2022 | 1 | Nathan Chen | US | 113.97 | 1 | 218.63 | 1 | 332.60 | OLY | Male |
| 2022 | 2 | Yuma Kagiyama | JP | 108.12 | 2 | 201.93 | 2 | 310.05 | OLY | Male |
| 2022 | 3 | Shoma Uno | JP | 105.90 | 3 | 187.10 | 5 | 293.00 | OLY | Male |
| 2022 | 4 | Yuzuru Hanyu | JP | 95.15 | 8 | 188.06 | 3 | 283.21 | OLY | Male |
| 2022 | 5 | Junhwan Cha | KR | 99.51 | 4 | 182.87 | 7 | 282.38 | OLY | Male |
| 2022 | 6 | Jason Brown | US | 97.24 | 6 | 184.00 | 6 | 281.24 | OLY | Male |
| 2022 | 7 | Daniel Grassl | IT | 90.64 | 12 | 187.43 | 4 | 278.07 | OLY | Male |
| 2022 | 8 | Evgeni Semenenko | RU | 95.76 | 7 | 178.37 | 9 | 274.13 | OLY | Male |
| 2022 | 9 | Boyang Jin | CN | 90.98 | 11 | 179.45 | 8 | 270.43 | OLY | Male |
| 2022 | 10 | Morisi Kvitelashvili | GE | 97.98 | 5 | 170.64 | 11 | 268.62 | OLY | Male |
| 2022 | 11 | Keegan Messing | CA | 93.24 | 9 | 172.37 | 10 | 265.61 | OLY | Male |
| 2022 | 12 | Kévin Aymoz | FR | 93.00 | 10 | 161.80 | 15 | 254.80 | OLY | Male |
| 2022 | 13 | Deniss Vasiljevs | LV | 85.30 | 16 | 167.41 | 12 | 252.71 | OLY | Male |
| 2022 | 14 | Adam Siao Him Fa | FR | 86.74 | 14 | 163.41 | 13 | 250.15 | OLY | Male |
| 2022 | 15 | Mark Kondratiuk | RU | 86.11 | 15 | 162.71 | 14 | 248.82 | OLY | Male |
| 2022 | 16 | Matteo Rizzo | IT | 88.63 | 13 | 158.90 | 17 | 247.53 | OLY | Male |
| 2022 | 17 | Brendan Kerry | AU | 84.79 | 17 | 160.01 | 16 | 244.80 | OLY | Male |
| 2022 | 18 | Vladimir Litvintsev | AZ | 84.15 | 18 | 155.04 | 19 | 239.19 | OLY | Male |
| 2022 | 19 | Andrei Mozalev | RU | 77.05 | 23 | 156.28 | 18 | 233.33 | OLY | Male |
| 2022 | 20 | Konstantin Milyukov | BY | 78.49 | 21 | 143.73 | 20 | 222.22 | OLY | Male |
| 2022 | 21 | Nikolaj Majorov | SE | 78.54 | 20 | 142.24 | 21 | 220.78 | OLY | Male |
| 2022 | 22 | Donovan Carrillo | MX | 79.69 | 19 | 138.44 | 22 | 218.13 | OLY | Male |
| 2022 | 23 | Lukas Britschgi | CH | 76.16 | 24 | 136.42 | 23 | 212.58 | OLY | Male |
| 2022 | 24 | Ivan Shmuratko | UA | 78.11 | 22 | 127.65 | 24 | 205.76 | OLY | Male |
| 2022 | 25 | Michal Březina | CZ | 75.19 | 25 | NA | NA | NA | OLY | Male |
| 2022 | 26 | Alexei Bychenko | IL | 68.01 | 26 | NA | NA | NA | OLY | Male |
| 2022 | 27 | Sihyeong Lee | KR | 65.69 | 27 | NA | NA | NA | OLY | Male |
| 2022 | 28 | Aleksandr Selevko | EE | 65.29 | 28 | NA | NA | NA | OLY | Male |
| 2022 | 29 | Roman Sadovsky | CA | 62.77 | 29 | NA | NA | NA | OLY | Male |
| 2022 | 1 | Alexandra Trusova | RU | 77.69 | 1 | 154.68 | 1 | 232.37 | GPUSA | Female |
| 2022 | 2 | Daria Usacheva | RU | 76.71 | 2 | 140.60 | 4 | 217.31 | GPUSA | Female |
| 2022 | 3 | Young You | KR | 70.73 | 5 | 146.24 | 2 | 216.97 | GPUSA | Female |
| 2022 | 4 | Kaori Sakamoto | JP | 71.16 | 4 | 144.77 | 3 | 215.93 | GPUSA | Female |
| 2022 | 5 | Ksenia Sinitsina | RU | 71.51 | 3 | 134.25 | 5 | 205.76 | GPUSA | Female |
| 2022 | 6 | Amber Glenn | US | 67.57 | 7 | 133.45 | 7 | 201.02 | GPUSA | Female |
| 2022 | 7 | Satoko Miyahara | JP | 66.36 | 8 | 134.15 | 6 | 200.51 | GPUSA | Female |
| 2022 | 8 | Yelim Kim | KR | 70.56 | 6 | 128.78 | 8 | 199.34 | GPUSA | Female |
| 2022 | 9 | Ekaterina Kurakova | PL | 61.36 | 11 | 127.24 | 9 | 188.60 | GPUSA | Female |
| 2022 | 10 | Starr Andrews | US | 61.94 | 10 | 115.69 | 11 | 177.63 | GPUSA | Female |
| 2022 | 11 | Yuhana Yokoi | JP | 54.77 | 12 | 119.30 | 10 | 174.07 | GPUSA | Female |
| 2022 | 12 | Audrey Shin | US | 62.82 | 9 | 97.96 | 12 | 160.78 | GPUSA | Female |
| 2022 | 1 | Vincent Zhou | US | 97.43 | 1 | 198.13 | 1 | 295.56 | GPUSA | Male |
| 2022 | 2 | Shoma Uno | JP | 89.07 | 2 | 181.61 | 3 | 270.68 | GPUSA | Male |
| 2022 | 3 | Nathan Chen | US | 82.89 | 4 | 186.48 | 2 | 269.37 | GPUSA | Male |
| 2022 | 4 | Shun Sato | JP | 80.52 | 5 | 166.53 | 4 | 247.05 | GPUSA | Male |
| 2022 | 5 | Jimmy Ma | US | 84.52 | 3 | 143.60 | 10 | 228.12 | GPUSA | Male |
| 2022 | 6 | Michal Březina | CZ | 75.43 | 6 | 152.04 | 5 | 227.47 | GPUSA | Male |
| 2022 | 7 | Daniel Grassl | IT | 70.88 | 8 | 150.55 | 6 | 221.43 | GPUSA | Male |
| 2022 | 8 | Nam Nguyen | CA | 74.32 | 7 | 145.28 | 9 | 219.60 | GPUSA | Male |
| 2022 | 9 | Adam Siao Him Fa | FR | 67.60 | 10 | 149.92 | 7 | 217.52 | GPUSA | Male |
| 2022 | 10 | Artur Danielian | RU | 68.74 | 9 | 146.19 | 8 | 214.93 | GPUSA | Male |
| 2022 | NA | Kévin Aymoz | FR | 58.14 | 11 | NA | NA | NA | GPUSA | Male |
| 2022 | 1 | Kamila Valieva | RU | 87.42 | 1 | 185.29 | 1 | 272.71 | GPRUS | Female |
| 2022 | 2 | Elizaveta Tuktamysheva | RU | 80.10 | 2 | 149.13 | 3 | 229.23 | GPRUS | Female |
| 2022 | 3 | Maiia Khromykh | RU | 64.72 | 5 | 154.97 | 2 | 219.69 | GPRUS | Female |
| 2022 | 4 | Mariah Bell | US | 69.37 | 3 | 140.98 | 4 | 210.35 | GPRUS | Female |
| 2022 | 5 | Loena Hendrickx | BE | 64.44 | 6 | 139.25 | 5 | 203.69 | GPRUS | Female |
| 2022 | 6 | Madeline Schizas | CA | 67.82 | 4 | 124.32 | 7 | 192.14 | GPRUS | Female |
| 2022 | 7 | Viktoriia Safonova | BY | 58.19 | 9 | 127.45 | 6 | 185.64 | GPRUS | Female |
| 2022 | 8 | Rino Matsuike | JP | 62.98 | 7 | 121.38 | 8 | 184.36 | GPRUS | Female |
| 2022 | 9 | Ekaterina Kurakova | PL | 56.43 | 11 | 119.21 | 9 | 175.64 | GPRUS | Female |
| 2022 | 10 | Ekaterina Ryabova | AZ | 58.87 | 8 | 116.37 | 10 | 175.24 | GPRUS | Female |
| 2022 | 11 | Eva Lotta Kiibus | EE | 49.26 | 12 | 113.85 | 11 | 163.11 | GPRUS | Female |
| 2022 | 12 | Olga Mikutina | AT | 57.09 | 10 | 104.00 | 12 | 161.09 | GPRUS | Female |
| 2022 | 1 | Morisi Kvitelashvili | GE | 95.37 | 2 | 170.96 | 3 | 266.33 | GPRUS | Male |
| 2022 | 2 | Mikhail Kolyada | RU | 84.48 | 4 | 180.16 | 1 | 264.64 | GPRUS | Male |
| 2022 | 3 | Kazuki Tomono | JP | 95.81 | 1 | 168.38 | 5 | 264.19 | GPRUS | Male |
| 2022 | 4 | Roman Sadovsky | CA | 84.59 | 3 | 169.21 | 4 | 253.80 | GPRUS | Male |
| 2022 | 5 | Matteo Rizzo | IT | 77.45 | 9 | 173.02 | 2 | 250.47 | GPRUS | Male |
| 2022 | 6 | Evgeni Semenenko | RU | 81.00 | 7 | 165.66 | 6 | 246.66 | GPRUS | Male |
| 2022 | 7 | Camden Pulkinen | US | 83.47 | 5 | 154.50 | 9 | 237.97 | GPRUS | Male |
| 2022 | 8 | Mark Kondratiuk | RU | 74.16 | 11 | 157.72 | 8 | 231.88 | GPRUS | Male |
| 2022 | 9 | Keiji Tanaka | JP | 76.69 | 10 | 153.06 | 10 | 229.75 | GPRUS | Male |
| 2022 | 10 | Michal Březina | CZ | 82.31 | 6 | 137.28 | 11 | 219.59 | GPRUS | Male |
| 2022 | 11 | Nika Egadze | GE | 50.35 | 12 | 159.82 | 7 | 210.17 | GPRUS | Male |
| 2022 | 12 | Brendan Kerry | AU | 80.48 | 8 | 123.71 | 12 | 204.19 | GPRUS | Male |
| 2022 | 1 | Kaori Sakamoto | JP | 76.56 | 1 | 146.78 | 1 | 223.34 | GPJPN | Female |
| 2022 | 2 | Mana Kawabe | JP | 73.88 | 2 | 131.56 | 4 | 205.44 | GPJPN | Female |
| 2022 | 3 | Young You | KR | 68.08 | 3 | 135.52 | 2 | 203.60 | GPJPN | Female |
| 2022 | 4 | Alysa Liu | US | 67.72 | 4 | 135.18 | 3 | 202.90 | GPJPN | Female |
| 2022 | 5 | Eunsoo Lim | KR | 65.23 | 5 | 121.45 | 6 | 186.68 | GPJPN | Female |
| 2022 | 6 | Rino Matsuike | JP | 63.34 | 7 | 122.83 | 5 | 186.17 | GPJPN | Female |
| 2022 | 7 | Amber Glenn | US | 63.43 | 6 | 112.40 | 8 | 175.83 | GPJPN | Female |
| 2022 | 8 | Nicole Schott | DE | 59.26 | 8 | 113.11 | 7 | 172.37 | GPJPN | Female |
| 2022 | 9 | Seoyeong Wi | KR | 58.23 | 9 | 112.31 | 9 | 170.54 | GPJPN | Female |
| 2022 | 1 | Shoma Uno | JP | 102.58 | 1 | 187.57 | 1 | 290.15 | GPJPN | Male |
| 2022 | 2 | Vincent Zhou | US | 99.51 | 2 | 161.18 | 6 | 260.69 | GPJPN | Male |
| 2022 | 3 | Junhwan Cha | KR | 95.92 | 3 | 163.68 | 5 | 259.60 | GPJPN | Male |
| 2022 | 4 | Makar Ignatov | RU | 90.54 | 4 | 166.66 | 4 | 257.20 | GPJPN | Male |
| 2022 | 5 | Matteo Rizzo | IT | 84.78 | 6 | 171.06 | 3 | 255.84 | GPJPN | Male |
| 2022 | 6 | Alexander Samarin | RU | 84.32 | 7 | 171.33 | 2 | 255.65 | GPJPN | Male |
| 2022 | 7 | Sota Yamamoto | JP | 86.05 | 5 | 152.85 | 8 | 238.90 | GPJPN | Male |
| 2022 | 8 | Kao Miura | JP | 76.62 | 8 | 156.27 | 7 | 232.89 | GPJPN | Male |
| 2022 | 9 | Tomoki Hiwatashi | US | 72.36 | 9 | 144.72 | 9 | 217.08 | GPJPN | Male |
| 2022 | 10 | Nam Nguyen | CA | 64.28 | 10 | 144.11 | 10 | 208.39 | GPJPN | Male |
| 2022 | 11 | Camden Pulkinen | US | 55.53 | 11 | 137.65 | 11 | 193.18 | GPJPN | Male |
| 2022 | 1 | Anna Shcherbakova | RU | 71.73 | 3 | 165.05 | 1 | 236.78 | GPITA | Female |
| 2022 | 2 | Maiia Khromykh | RU | 72.04 | 2 | 154.31 | 2 | 226.35 | GPITA | Female |
| 2022 | 3 | Loena Hendrickx | BE | 73.52 | 1 | 145.53 | 3 | 219.05 | GPITA | Female |
| 2022 | 4 | Mai Mihara | JP | 70.46 | 5 | 144.49 | 4 | 214.95 | GPITA | Female |
| 2022 | 5 | Satoko Miyahara | JP | 70.85 | 4 | 138.72 | 5 | 209.57 | GPITA | Female |
| 2022 | 6 | Yelim Kim | KR | 62.78 | 7 | 130.72 | 6 | 193.50 | GPITA | Female |
| 2022 | 7 | Sofia Samodurova | RU | 58.68 | 9 | 121.91 | 7 | 180.59 | GPITA | Female |
| 2022 | 8 | Eunsoo Lim | KR | 67.03 | 6 | 112.55 | 8 | 179.58 | GPITA | Female |
| 2022 | 9 | Yi Zhu | CN | 60.00 | 8 | 111.25 | 9 | 171.25 | GPITA | Female |
| 2022 | 10 | Nicole Schott | DE | 58.33 | 10 | 108.87 | 10 | 167.20 | GPITA | Female |
| 2022 | 11 | Lara Naki Gutmann | IT | 54.83 | 11 | 103.74 | 11 | 158.57 | GPITA | Female |
| 2022 | 12 | Lucrezia Beccari | IT | 53.35 | 12 | 94.94 | 12 | 148.29 | GPITA | Female |
| 2022 | 1 | Yuma Kagiyama | JP | 80.53 | 7 | 197.49 | 1 | 278.02 | GPITA | Male |
| 2022 | 2 | Mikhail Kolyada | RU | 92.30 | 4 | 181.25 | 2 | 273.55 | GPITA | Male |
| 2022 | 3 | Daniel Grassl | IT | 95.67 | 2 | 173.33 | 3 | 269.00 | GPITA | Male |
| 2022 | 4 | Deniss Vasiljevs | LV | 85.09 | 5 | 163.35 | 4 | 248.44 | GPITA | Male |
| 2022 | 5 | Junhwan Cha | KR | 95.56 | 3 | 152.18 | 6 | 247.74 | GPITA | Male |
| 2022 | 6 | Kazuki Tomono | JP | 83.91 | 6 | 161.20 | 5 | 245.11 | GPITA | Male |
| 2022 | 7 | Boyang Jin | CN | 97.89 | 1 | 144.38 | 9 | 242.27 | GPITA | Male |
| 2022 | 8 | Peter Gumennik | RU | 76.81 | 9 | 149.95 | 7 | 226.76 | GPITA | Male |
| 2022 | 9 | Dmitri Aliev | RU | 71.07 | 10 | 146.60 | 8 | 217.67 | GPITA | Male |
| 2022 | 10 | Yudong Chen | CN | 78.79 | 8 | 122.17 | 10 | 200.96 | GPITA | Male |
| 2022 | 11 | Gabriele Frangipani | IT | 55.09 | 12 | 112.51 | 11 | 167.60 | GPITA | Male |
| 2022 | NA | Paul Fentz | DE | 57.17 | 11 | NA | NA | NA | GPITA | Male |
| 2022 | 1 | Anna Shcherbakova | RU | 77.94 | 1 | 151.75 | 1 | 229.69 | GPFRA | Female |
| 2022 | 2 | Alena Kostornaia | RU | 76.44 | 2 | 145.41 | 2 | 221.85 | GPFRA | Female |
| 2022 | 3 | Wakaba Higuchi | JP | 63.87 | 6 | 141.04 | 3 | 204.91 | GPFRA | Female |
| 2022 | 4 | Ksenia Sinitsina | RU | 69.89 | 3 | 128.87 | 6 | 198.76 | GPFRA | Female |
| 2022 | 5 | Karen Chen | US | 64.67 | 5 | 129.33 | 5 | 194.00 | GPFRA | Female |
| 2022 | 6 | Mariah Bell | US | 60.81 | 10 | 129.98 | 4 | 190.79 | GPFRA | Female |
| 2022 | 7 | Ekaterina Ryabova | AZ | 63.34 | 7 | 123.31 | 8 | 186.65 | GPFRA | Female |
| 2022 | 8 | Yeonjeong Park | KR | 67.00 | 4 | 119.11 | 9 | 186.11 | GPFRA | Female |
| 2022 | 9 | Yuhana Yokoi | JP | 52.32 | 11 | 124.61 | 7 | 176.93 | GPFRA | Female |
| 2022 | 10 | Haein Lee | KR | 63.18 | 8 | 108.14 | 10 | 171.32 | GPFRA | Female |
| 2022 | 11 | Léa Serna | FR | 62.75 | 9 | 107.58 | 11 | 170.33 | GPFRA | Female |
| 2022 | 1 | Yuma Kagiyama | JP | 100.64 | 1 | 185.77 | 1 | 286.41 | GPFRA | Male |
| 2022 | 2 | Shun Sato | JP | 87.82 | 4 | 177.17 | 3 | 264.99 | GPFRA | Male |
| 2022 | 3 | Jason Brown | US | 89.39 | 3 | 174.81 | 4 | 264.20 | GPFRA | Male |
| 2022 | 4 | Deniss Vasiljevs | LV | 89.76 | 2 | 164.72 | 7 | 254.48 | GPFRA | Male |
| 2022 | 5 | Dmitri Aliev | RU | 85.05 | 5 | 168.51 | 5 | 253.56 | GPFRA | Male |
| 2022 | 6 | Keegan Messing | CA | 85.03 | 6 | 168.03 | 6 | 253.06 | GPFRA | Male |
| 2022 | 7 | Andrei Mozalev | RU | 68.77 | 9 | 179.77 | 2 | 248.54 | GPFRA | Male |
| 2022 | 8 | Adam Siao Him Fa | FR | 84.47 | 7 | 158.82 | 9 | 243.29 | GPFRA | Male |
| 2022 | 9 | Kévin Aymoz | FR | 63.98 | 12 | 164.10 | 8 | 228.08 | GPFRA | Male |
| 2022 | 10 | Artur Danielian | RU | 76.81 | 8 | 144.69 | 11 | 221.50 | GPFRA | Male |
| 2022 | 11 | Romain Ponsart | FR | 66.38 | 10 | 145.89 | 10 | 212.27 | GPFRA | Male |
| 2022 | 12 | Gabriele Frangipani | IT | 66.33 | 11 | 117.94 | 12 | 184.27 | GPFRA | Male |
| 2022 | 1 | Kamila Valieva | RU | 84.19 | 1 | 180.89 | 1 | 265.08 | GPCAN | Female |
| 2022 | 2 | Elizaveta Tuktamysheva | RU | 81.24 | 2 | 151.64 | 2 | 232.88 | GPCAN | Female |
| 2022 | 3 | Alena Kostornaia | RU | 75.58 | 3 | 138.96 | 4 | 214.54 | GPCAN | Female |
| 2022 | 4 | Mai Mihara | JP | 67.89 | 7 | 142.12 | 3 | 210.01 | GPCAN | Female |
| 2022 | 5 | Alysa Liu | US | 73.63 | 4 | 132.90 | 7 | 206.53 | GPCAN | Female |
| 2022 | 6 | Wakaba Higuchi | JP | 69.41 | 5 | 135.86 | 5 | 205.27 | GPCAN | Female |
| 2022 | 7 | Haein Lee | KR | 62.63 | 8 | 127.37 | 8 | 190.00 | GPCAN | Female |
| 2022 | 8 | Madeline Schizas | CA | 62.61 | 9 | 123.95 | 9 | 186.56 | GPCAN | Female |
| 2022 | 9 | Mana Kawabe | JP | 53.30 | 12 | 133.22 | 6 | 186.52 | GPCAN | Female |
| 2022 | 10 | Karen Chen | US | 68.74 | 6 | 114.67 | 10 | 183.41 | GPCAN | Female |
| 2022 | 11 | Emily Bausback | CA | 59.53 | 10 | 100.35 | 11 | 159.88 | GPCAN | Female |
| 2022 | 12 | Alison Schumacher | CA | 55.47 | 11 | 95.72 | 12 | 151.19 | GPCAN | Female |
| 2022 | 1 | Nathan Chen | US | 106.72 | 1 | 200.46 | 1 | 307.18 | GPCAN | Male |
| 2022 | 2 | Jason Brown | US | 94.00 | 2 | 165.55 | 3 | 259.55 | GPCAN | Male |
| 2022 | 3 | Evgeni Semenenko | RU | 87.71 | 5 | 168.30 | 2 | 256.01 | GPCAN | Male |
| 2022 | 4 | Makar Ignatov | RU | 89.79 | 4 | 154.38 | 5 | 244.17 | GPCAN | Male |
| 2022 | 5 | Keegan Messing | CA | 93.28 | 3 | 145.06 | 10 | 238.34 | GPCAN | Male |
| 2022 | 6 | Morisi Kvitelashvili | GE | 71.60 | 12 | 161.27 | 4 | 232.87 | GPCAN | Male |
| 2022 | 7 | Sota Yamamoto | JP | 78.78 | 7 | 146.96 | 8 | 225.74 | GPCAN | Male |
| 2022 | 8 | Alexander Samarin | RU | 78.55 | 8 | 145.65 | 9 | 224.20 | GPCAN | Male |
| 2022 | 9 | Conrad Orzel | CA | 73.19 | 9 | 149.56 | 6 | 222.75 | GPCAN | Male |
| 2022 | 10 | Keiji Tanaka | JP | 78.83 | 6 | 143.37 | 12 | 222.20 | GPCAN | Male |
| 2022 | 11 | Tomoki Hiwatashi | US | 72.92 | 11 | 148.85 | 7 | 221.77 | GPCAN | Male |
| 2022 | 12 | Roman Sadovsky | CA | 72.94 | 10 | 144.79 | 11 | 217.73 | GPCAN | Male |
| 2022 | 1 | Kamila Valieva | RU | 90.45 | 1 | 168.61 | 1 | 259.06 | EC | Female |
| 2022 | 2 | Anna Shcherbakova | RU | 69.05 | 4 | 168.37 | 2 | 237.42 | EC | Female |
| 2022 | 3 | Alexandra Trusova | RU | 75.13 | 3 | 159.23 | 3 | 234.36 | EC | Female |
| 2022 | 4 | Loena Hendrickx | BE | 76.25 | 2 | 131.72 | 5 | 207.97 | EC | Female |
| 2022 | 5 | Ekaterina Kurakova | PL | 67.47 | 5 | 137.26 | 4 | 204.73 | EC | Female |
| 2022 | 6 | Ekaterina Ryabova | AZ | 65.47 | 7 | 131.28 | 6 | 196.75 | EC | Female |
| 2022 | 7 | Anastasiia Gubanova | GE | 67.02 | 6 | 121.15 | 9 | 188.17 | EC | Female |
| 2022 | 8 | Niina Petrõkina | EE | 58.30 | 17 | 128.77 | 7 | 187.07 | EC | Female |
| 2022 | 9 | Viktoriia Safonova | BY | 63.07 | 8 | 122.34 | 8 | 185.41 | EC | Female |
| 2022 | 10 | Alexia Paganini | CH | 62.32 | 9 | 115.78 | 10 | 178.10 | EC | Female |
| 2022 | 11 | Eva Lotta Kiibus | EE | 59.16 | 15 | 112.48 | 11 | 171.64 | EC | Female |
| 2022 | 12 | Léa Serna | FR | 62.16 | 10 | 108.84 | 13 | 171.00 | EC | Female |
| 2022 | 13 | Nicole Schott | DE | 61.86 | 11 | 108.32 | 14 | 170.18 | EC | Female |
| 2022 | 14 | Josefin Taljegård | SE | 58.24 | 18 | 106.06 | 15 | 164.30 | EC | Female |
| 2022 | 15 | Olga Mikutina | AT | 60.16 | 12 | 103.85 | 17 | 164.01 | EC | Female |
| 2022 | 16 | Lara Naki Gutmann | IT | 52.94 | 23 | 111.05 | 12 | 163.99 | EC | Female |
| 2022 | 17 | Natasha Mckay | GB | 57.07 | 19 | 104.67 | 16 | 161.74 | EC | Female |
| 2022 | 18 | Jenni Saarinen | FI | 58.93 | 16 | 101.39 | 19 | 160.32 | EC | Female |
| 2022 | 19 | Yasmine Kimiko Yamada | CH | 56.54 | 21 | 102.64 | 18 | 159.18 | EC | Female |
| 2022 | 20 | Alexandra Feigin | BG | 56.78 | 20 | 98.78 | 20 | 155.56 | EC | Female |
| 2022 | 21 | Eliska Brezinova | CZ | 59.62 | 13 | 95.62 | 21 | 155.24 | EC | Female |
| 2022 | 22 | Aleksandra Golovkina | LT | 52.63 | 24 | 89.57 | 22 | 142.20 | EC | Female |
| 2022 | 23 | Regina Schermann | HU | 54.43 | 22 | 78.99 | 23 | 133.42 | EC | Female |
| 2022 | NA | Marina Piredda | IT | 59.53 | 14 | NA | NA | NA | EC | Female |
| 2022 | 25 | Anete Lace | LV | 49.75 | 25 | NA | NA | NA | EC | Female |
| 2022 | 26 | Oona Ounasvuori | FI | 49.13 | 26 | NA | NA | NA | EC | Female |
| 2022 | 27 | Lindsay Van Zundert | NL | 48.92 | 27 | NA | NA | NA | EC | Female |
| 2022 | 28 | Daša Grm | SI | 47.85 | 28 | NA | NA | NA | EC | Female |
| 2022 | 29 | Antonina Dubinina | RS | 47.77 | 29 | NA | NA | NA | EC | Female |
| 2022 | 30 | Taylor Lynn Morris | IL | 46.60 | 30 | NA | NA | NA | EC | Female |
| 2022 | 31 | Linnea Kilsand | NO | 45.51 | 31 | NA | NA | NA | EC | Female |
| 2022 | 32 | Marilena Kitromilis | CY | 44.03 | 32 | NA | NA | NA | EC | Female |
| 2022 | 33 | Alexandra Michaela Filcová | SK | 43.56 | 33 | NA | NA | NA | EC | Female |
| 2022 | 34 | Aldís Kara Bergsdóttír | IS | 42.23 | 34 | NA | NA | NA | EC | Female |
| 2022 | 35 | Sinem Pekder | TR | 42.16 | 35 | NA | NA | NA | EC | Female |
| 2022 | 36 | Maia Sørensen | DK | 40.93 | 36 | NA | NA | NA | EC | Female |
| 2022 | 1 | Mark Kondratiuk | RU | 99.06 | 2 | 187.50 | 1 | 286.56 | EC | Male |
| 2022 | 2 | Daniel Grassl | IT | 91.75 | 5 | 182.73 | 2 | 274.48 | EC | Male |
| 2022 | 3 | Deniss Vasiljevs | LV | 90.24 | 6 | 181.84 | 3 | 272.08 | EC | Male |
| 2022 | 4 | Andrei Mozalev | RU | 99.76 | 1 | 165.93 | 6 | 265.69 | EC | Male |
| 2022 | 5 | Evgeni Semenenko | RU | 99.04 | 3 | 160.96 | 9 | 260.00 | EC | Male |
| 2022 | 6 | Morisi Kvitelashvili | GE | 92.76 | 4 | 161.15 | 8 | 253.91 | EC | Male |
| 2022 | 7 | Kévin Aymoz | FR | 80.39 | 10 | 171.82 | 4 | 252.21 | EC | Male |
| 2022 | 8 | Vladimir Litvintsev | AZ | 83.46 | 7 | 161.24 | 7 | 244.70 | EC | Male |
| 2022 | 9 | Gabriele Frangipani | IT | 81.79 | 9 | 157.16 | 10 | 238.95 | EC | Male |
| 2022 | 10 | Michal Březina | CZ | 71.60 | 15 | 166.78 | 5 | 238.38 | EC | Male |
| 2022 | 11 | Lukas Britschgi | CH | 72.96 | 13 | 145.95 | 11 | 218.91 | EC | Male |
| 2022 | 12 | Ivan Shmuratko | UA | 82.13 | 8 | 132.44 | 15 | 214.57 | EC | Male |
| 2022 | 13 | Nikita Starostin | DE | 72.12 | 14 | 142.28 | 12 | 214.40 | EC | Male |
| 2022 | 14 | Arlet Levandi | EE | 70.04 | 17 | 138.48 | 13 | 208.52 | EC | Male |
| 2022 | 15 | Nikolaj Memola | IT | 73.98 | 12 | 132.55 | 14 | 206.53 | EC | Male |
| 2022 | 16 | Paul Fentz | DE | 76.76 | 11 | 129.30 | 16 | 206.06 | EC | Male |
| 2022 | 17 | Maurizio Zandrón | AT | 70.75 | 16 | 123.16 | 20 | 193.91 | EC | Male |
| 2022 | 18 | Kornel Witkowski | PL | 66.26 | 23 | 127.03 | 17 | 193.29 | EC | Male |
| 2022 | 19 | Valtter Virtanen | FI | 67.34 | 20 | 123.63 | 18 | 190.97 | EC | Male |
| 2022 | 20 | Davidé Lewton Brain | MC | 67.31 | 21 | 123.36 | 19 | 190.67 | EC | Male |
| 2022 | 21 | Konstantin Milyukov | BY | 69.10 | 18 | 113.49 | 21 | 182.59 | EC | Male |
| 2022 | 22 | Tomás Guarino Sabaté | ES | 66.20 | 24 | 112.47 | 22 | 178.67 | EC | Male |
| 2022 | 23 | Burak Demirboga | TR | 67.30 | 22 | 100.73 | 23 | 168.03 | EC | Male |
| 2022 | 24 | Slavik Hayrapetyan | AM | 67.75 | 19 | 100.09 | 24 | 167.84 | EC | Male |
| 2022 | 25 | Adam Hagara | SK | 65.23 | 25 | NA | NA | NA | EC | Male |
| 2022 | 26 | Graham Newberry | GB | 64.49 | 26 | NA | NA | NA | EC | Male |
| 2022 | 27 | Matyas Belohradsky | CZ | 64.38 | 27 | NA | NA | NA | EC | Male |
| 2022 | 28 | Nika Egadze | GE | 63.60 | 28 | NA | NA | NA | EC | Male |
| 2022 | 29 | Daniels Kockers | LV | 56.10 | 29 | NA | NA | NA | EC | Male |
| 2022 | 30 | Conor Stakelum | IE | 56.00 | 30 | NA | NA | NA | EC | Male |
| 2022 | 31 | Jari Kessler | HR | 55.82 | 31 | NA | NA | NA | EC | Male |
| 2022 | 32 | Andras Csernoch | HU | 54.88 | 32 | NA | NA | NA | EC | Male |
| 2022 | 33 | Larry Loupolover | BG | 45.67 | 33 | NA | NA | NA | EC | Male |
| 2022 | 1 | Mai Mihara | JP | 72.62 | 1 | 145.41 | 1 | 218.03 | 4CC | Female |
| 2022 | 2 | Haein Lee | KR | 69.97 | 2 | 143.55 | 2 | 213.52 | 4CC | Female |
| 2022 | 3 | Yelim Kim | KR | 68.93 | 3 | 140.98 | 4 | 209.91 | 4CC | Female |
| 2022 | 4 | Audrey Shin | US | 67.20 | 5 | 136.66 | 5 | 203.86 | 4CC | Female |
| 2022 | 5 | Rino Matsuike | JP | 60.16 | 8 | 142.05 | 3 | 202.21 | 4CC | Female |
| 2022 | 6 | Young You | KR | 67.86 | 4 | 130.70 | 7 | 198.56 | 4CC | Female |
| 2022 | 7 | Yuhana Yokoi | JP | 53.93 | 12 | 131.41 | 6 | 185.34 | 4CC | Female |
| 2022 | 8 | Gabriella Izzo | US | 63.19 | 7 | 116.87 | 8 | 180.06 | 4CC | Female |
| 2022 | 9 | Starr Andrews | US | 66.60 | 6 | 106.41 | 12 | 173.01 | 4CC | Female |
| 2022 | 10 | Gabrielle Daleman | CA | 59.01 | 9 | 113.97 | 9 | 172.98 | 4CC | Female |
| 2022 | 11 | Alison Schumacher | CA | 57.36 | 11 | 111.06 | 10 | 168.42 | 4CC | Female |
| 2022 | 12 | Kailani Craine | AU | 57.46 | 10 | 106.56 | 11 | 164.02 | 4CC | Female |
| 2022 | 13 | Véronik Mallet | CA | 53.77 | 13 | 98.10 | 13 | 151.87 | 4CC | Female |
| 2022 | 14 | Jocelyn Hong | NZ | 48.60 | 18 | 97.02 | 14 | 145.62 | 4CC | Female |
| 2022 | 15 | Tzu-Han Ting | TW | 49.15 | 17 | 96.42 | 15 | 145.57 | 4CC | Female |
| 2022 | 16 | Sofia Lexi Jacqueline Frank | PH | 52.74 | 14 | 86.52 | 17 | 139.26 | 4CC | Female |
| 2022 | 17 | Victoria Alcantara | AU | 49.73 | 16 | 88.53 | 16 | 138.26 | 4CC | Female |
| 2022 | 18 | Andrea Montesinos Cantú | MX | 47.36 | 19 | 85.67 | 18 | 133.03 | 4CC | Female |
| 2022 | 19 | Eugenia Garza Martinez | MX | 51.16 | 15 | 77.57 | 20 | 128.73 | 4CC | Female |
| 2022 | 20 | Tara Prasad | IN | 43.31 | 20 | 84.62 | 19 | 127.93 | 4CC | Female |
| 2022 | 1 | Junhwan Cha | KR | 98.96 | 1 | 174.26 | 1 | 273.22 | 4CC | Male |
| 2022 | 2 | Kazuki Tomono | JP | 97.10 | 2 | 171.89 | 2 | 268.99 | 4CC | Male |
| 2022 | 3 | Kao Miura | JP | 88.37 | 3 | 162.70 | 3 | 251.07 | 4CC | Male |
| 2022 | 4 | Sena Miyake | JP | 79.67 | 5 | 160.35 | 4 | 240.02 | 4CC | Male |
| 2022 | 5 | Mikhail Shaidorov | KZ | 75.96 | 8 | 158.71 | 5 | 234.67 | 4CC | Male |
| 2022 | 6 | Brendan Kerry | AU | 81.12 | 4 | 146.45 | 8 | 227.57 | 4CC | Male |
| 2022 | 7 | Sihyeong Lee | KR | 79.13 | 6 | 144.05 | 11 | 223.18 | 4CC | Male |
| 2022 | 8 | Tomoki Hiwatashi | US | 77.51 | 7 | 144.86 | 10 | 222.37 | 4CC | Male |
| 2022 | 9 | Joseph Phan | CA | 69.70 | 10 | 151.15 | 6 | 220.85 | 4CC | Male |
| 2022 | 10 | Jimmy Ma | US | 69.98 | 9 | 145.14 | 9 | 215.12 | 4CC | Male |
| 2022 | 11 | Corey Circelli | CA | 69.57 | 11 | 143.45 | 12 | 213.02 | 4CC | Male |
| 2022 | 12 | Camden Pulkinen | US | 57.58 | 14 | 146.81 | 7 | 204.39 | 4CC | Male |
| 2022 | 13 | Dias Jirenbayev | KZ | 66.92 | 12 | 126.00 | 13 | 192.92 | 4CC | Male |
| 2022 | 14 | Jaeseok Kyeong | KR | 63.78 | 13 | 124.19 | 14 | 187.97 | 4CC | Male |
| 2022 | 15 | James Min | AU | 54.35 | 15 | 100.67 | 15 | 155.02 | 4CC | Male |
| 2022 | 16 | Jordan Dodds | AU | 47.47 | 16 | 91.68 | 16 | 139.15 | 4CC | Male |
| 2022 | NA | Harrison Jon Yen Wong | HK | 43.95 | 17 | NA | NA | NA | 4CC | Male |
| 2019 | 12 | Yi Christy Leung | HK | 53.93 | 15 | 110.86 | 11 | 164.79 | 4CC | Female |
| 2019 | 8 | Yelim Kim | KR | 64.42 | 9 | 123.51 | 7 | 187.93 | 4CC | Female |
| 2019 | 3 | Vincent Zhou | US | 100.18 | 1 | 172.04 | 5 | 272.22 | 4CC | Male |
| 2019 | 9 | Véronik Mallet | CA | 54.97 | 12 | 115.49 | 9 | 170.46 | 4CC | Female |
| 2019 | 8 | Tomoki Hiwatashi | US | 76.95 | 9 | 159.84 | 7 | 236.79 | 4CC | Male |
| 2019 | 11 | Ting Cui | US | 66.73 | 7 | 98.11 | 14 | 164.84 | 4CC | Female |
| 2019 | 15 | Sihyeong Lee | KR | 56.03 | 21 | 127.95 | 13 | 183.98 | 4CC | Male |
| 2019 | 1 | Shoma Uno | JP | 91.76 | 4 | 197.36 | 1 | 289.12 | 4CC | Male |
| 2019 | 1 | Rika Kihira | JP | 68.85 | 5 | 153.14 | 1 | 221.99 | 4CC | Female |
| 2019 | 23 | Nikita Manko | KZ | 51.15 | 22 | 87.25 | 23 | 138.40 | 4CC | Male |
| 2019 | 11 | Nicolas Nadeau | CA | 74.44 | 11 | 135.21 | 11 | 209.65 | 4CC | Male |
| 2019 | 10 | Nam Nguyen | CA | 79.55 | 8 | 136.94 | 10 | 216.49 | 4CC | Male |
| 2019 | 18 | Micah Tang | TW | 62.95 | 17 | 111.64 | 19 | 174.59 | 4CC | Male |
| 2019 | 19 | Micah Kai Lynette | TH | 57.45 | 19 | 116.99 | 16 | 174.44 | 4CC | Male |
| 2019 | 24 | Mark Webster | AU | 50.24 | 24 | 76.17 | 24 | 126.41 | 4CC | Male |
| 2019 | 6 | Mariah Bell | US | 70.02 | 3 | 123.92 | 6 | 193.94 | 4CC | Female |
| 2019 | 3 | Mai Mihara | JP | 65.15 | 8 | 141.97 | 2 | 207.12 | 4CC | Female |
| 2019 | 22 | Leslie Man Cheuk Ip | HK | 50.40 | 23 | 96.20 | 21 | 146.60 | 4CC | Male |
| 2019 | 10 | Larkyn Austman | CA | 54.99 | 11 | 110.22 | 12 | 165.21 | 4CC | Female |
| 2019 | 7 | Keiji Tanaka | JP | 83.93 | 7 | 167.61 | 6 | 251.54 | 4CC | Male |
| 2019 | 4 | Keegan Messing | CA | 88.18 | 5 | 179.43 | 3 | 267.61 | 4CC | Male |
| 2019 | 12 | Kazuki Tomono | JP | 74.16 | 12 | 132.25 | 12 | 206.41 | 4CC | Male |
| 2019 | 4 | Kaori Sakamoto | JP | 73.36 | 2 | 133.43 | 4 | 206.79 | 4CC | Female |
| 2019 | 15 | Kailani Craine | AU | 60.64 | 10 | 88.88 | 17 | 149.52 | 4CC | Female |
| 2019 | 25 | Kai Xiang Chew | MY | 46.38 | 25 | NA | NA | NA | 4CC | Male |
| 2019 | 6 | Junhwan Cha | KR | 97.33 | 2 | 158.50 | 8 | 255.83 | 4CC | Male |
| 2019 | 14 | Junehyoung Lee | KR | 64.19 | 16 | 123.91 | 14 | 188.10 | 4CC | Male |
| 2019 | 20 | Julian Zhi-Jie Yee | MY | 61.23 | 18 | 112.87 | 17 | 174.10 | 4CC | Male |
| 2019 | 21 | Joanna So | HK | 50.00 | 18 | 67.82 | 21 | 117.82 | 4CC | Female |
| 2019 | 5 | Jason Brown | US | 86.57 | 6 | 172.32 | 4 | 258.89 | 4CC | Male |
| 2019 | 17 | Isadora Williams | BR | 47.92 | 19 | 90.34 | 16 | 138.26 | 4CC | Female |
| 2019 | 14 | Hongyi Chen | CN | 54.44 | 13 | 96.06 | 15 | 150.50 | 4CC | Female |
| 2019 | 16 | He Zhang | CN | 67.38 | 15 | 112.51 | 18 | 179.89 | 4CC | Male |
| 2019 | 21 | Harrison Jon Yen Wong | HK | 56.27 | 20 | 92.27 | 22 | 148.54 | 4CC | Male |
| 2019 | 13 | Hanul Kim | KR | 51.44 | 17 | 111.04 | 10 | 162.48 | 4CC | Female |
| 2019 | 7 | Eunsoo Lim | KR | 69.14 | 4 | 122.71 | 8 | 191.85 | 4CC | Female |
| 2019 | 2 | Elizabet Tursynbayeva | KZ | 68.09 | 6 | 139.37 | 3 | 207.46 | 4CC | Female |
| 2019 | 17 | Donovan Carrillo | MX | 71.16 | 14 | 103.54 | 20 | 174.70 | 4CC | Male |
| 2019 | NA | Brooklee Han | AU | 54.22 | 14 | NA | NA | NA | 4CC | Female |
| 2019 | 9 | Brendan Kerry | AU | 76.81 | 10 | 147.63 | 9 | 224.44 | 4CC | Male |
| 2019 | 5 | Bradie Tennell | US | 73.91 | 1 | 128.16 | 5 | 202.07 | 4CC | Female |
| 2019 | 2 | Boyang Jin | CN | 92.17 | 3 | 181.34 | 2 | 273.51 | 4CC | Male |
| 2019 | 13 | Andrew Dodds | AU | 71.91 | 13 | 119.49 | 15 | 191.40 | 4CC | Male |
| 2019 | 20 | Andrea Montesinos Cantú | MX | 42.92 | 22 | 81.59 | 20 | 124.51 | 4CC | Female |
| 2019 | 19 | Amy Lin | TW | 46.99 | 20 | 87.57 | 18 | 134.56 | 4CC | Female |
| 2019 | 18 | Alisson Perticheto | PH | 51.66 | 16 | 85.31 | 19 | 136.97 | 4CC | Female |
| 2019 | 16 | Alaine Chartrand | CA | 45.89 | 21 | 101.65 | 13 | 147.54 | 4CC | Female |
| 2019 | 15 | Yasmine Kimiko Yamada | CH | 51.21 | 18 | 99.91 | 14 | 151.12 | EC | Female |
| 2019 | 30 | Yakau Zenko | BY | 56.38 | 30 | NA | NA | NA | EC | Male |
| 2019 | 16 | Vladimir Litvintsev | AZ | 73.60 | 14 | 130.68 | 15 | 204.28 | EC | Male |
| 2019 | 3 | Viveca Lindfors | FI | 65.61 | 4 | 128.79 | 3 | 194.40 | EC | Female |
| 2019 | 31 | Valentina Matos | ES | 42.86 | 31 | NA | NA | NA | EC | Female |
| 2019 | 36 | Thomas Kennes | NL | 48.57 | 36 | NA | NA | NA | EC | Male |
| 2019 | 4 | Stanislava Konstantinova | RU | 56.76 | 11 | 132.96 | 2 | 189.72 | EC | Female |
| 2019 | 26 | Sophia Schaller | AT | 44.20 | 26 | NA | NA | NA | EC | Female |
| 2019 | 18 | Sondre Oddvoll Bøe | NO | 66.03 | 20 | 125.98 | 18 | 192.01 | EC | Male |
| 2019 | 1 | Sofia Samodurova | RU | 72.88 | 2 | 140.96 | 1 | 213.84 | EC | Female |
| 2019 | 26 | Slavik Hayrapetyan | AM | 59.87 | 26 | NA | NA | NA | EC | Male |
| 2019 | 30 | Silvia Hugec | SK | 43.33 | 30 | NA | NA | NA | EC | Female |
| 2019 | 32 | Roman Galay | FI | 55.40 | 32 | NA | NA | NA | EC | Male |
| 2019 | 23 | Pernille Sørensen | DK | 50.59 | 20 | 81.19 | 23 | 131.78 | EC | Female |
| 2019 | 32 | Paulina Ramanauskaite | LT | 42.31 | 32 | NA | NA | NA | EC | Female |
| 2019 | 15 | Paul Fentz | DE | 69.70 | 17 | 140.26 | 12 | 209.96 | EC | Male |
| 2019 | 27 | Nikolaj Majorov | SE | 59.68 | 27 | NA | NA | NA | EC | Male |
| 2019 | 16 | Nicole Schott | DE | 50.68 | 19 | 98.58 | 16 | 149.26 | EC | Female |
| 2019 | 9 | Nicole Rajičová | SK | 64.08 | 5 | 104.95 | 12 | 169.03 | EC | Female |
| 2019 | 29 | Nicky-Leo Obreykov | BG | 56.54 | 29 | NA | NA | NA | EC | Male |
| 2019 | 21 | Nathalie Weinzierl | DE | 46.09 | 24 | 88.49 | 21 | 134.58 | EC | Female |
| 2019 | 20 | Natasha Mckay | GB | 48.20 | 22 | 91.88 | 19 | 140.08 | EC | Female |
| 2019 | 10 | Morisi Kvitelashvili | GE | 73.04 | 15 | 146.75 | 7 | 219.79 | EC | Male |
| 2019 | 5 | Mikhail Kolyada | RU | 100.49 | 1 | 140.38 | 11 | 240.87 | EC | Male |
| 2019 | 7 | Michal Březina | CZ | 83.66 | 8 | 150.59 | 6 | 234.25 | EC | Male |
| 2019 | 35 | Michael Neuman | SK | 53.38 | 35 | NA | NA | NA | EC | Male |
| 2019 | 14 | Maxim Kovtun | RU | 87.70 | 5 | 128.48 | 16 | 216.18 | EC | Male |
| 2019 | 19 | Matyas Belohradsky | CZ | 67.40 | 18 | 123.82 | 21 | 191.22 | EC | Male |
| 2019 | 3 | Matteo Rizzo | IT | 81.41 | 10 | 165.67 | 3 | 247.08 | EC | Male |
| 2019 | 7 | Maé-Bérénice Méité | FR | 58.95 | 8 | 118.15 | 5 | 177.10 | EC | Female |
| 2019 | 31 | Lukas Britschgi | CH | 55.86 | 31 | NA | NA | NA | EC | Male |
| 2019 | 19 | Lucrezia Gennaro | IT | 52.91 | 16 | 90.19 | 20 | 143.10 | EC | Female |
| 2019 | 20 | Luc Maierhofer | AT | 63.63 | 21 | 125.37 | 19 | 189.00 | EC | Male |
| 2019 | 5 | Laurine Lecavelier | FR | 63.29 | 6 | 116.76 | 6 | 180.05 | EC | Female |
| 2019 | 29 | Lara Naki Gutmann | IT | 43.96 | 29 | NA | NA | NA | EC | Female |
| 2019 | 28 | Kyarha Van Tiel | NL | 44.00 | 28 | NA | NA | NA | EC | Female |
| 2019 | 4 | Kévin Aymoz | FR | 88.02 | 4 | 158.32 | 4 | 246.34 | EC | Male |
| 2019 | 14 | Julia Sauter | RO | 54.29 | 14 | 98.86 | 15 | 153.15 | EC | Female |
| 2019 | 1 | Javier Fernández | ES | 91.84 | 3 | 179.75 | 1 | 271.59 | EC | Male |
| 2019 | 13 | Ivett Tóth | HU | 54.90 | 13 | 105.93 | 10 | 160.83 | EC | Female |
| 2019 | 22 | Ivan Shmuratko | UA | 67.26 | 19 | 111.03 | 24 | 178.29 | EC | Male |
| 2019 | 23 | Irakli Maysuradze | GE | 60.89 | 24 | 113.54 | 22 | 174.43 | EC | Male |
| 2019 | 25 | Ihor Reznichenko | PL | 59.99 | 25 | NA | NA | NA | EC | Male |
| 2019 | 34 | Héctor Alonso Serrano | ES | 53.94 | 34 | NA | NA | NA | EC | Male |
| 2019 | 34 | Hana Cvijanović | HR | 38.00 | 34 | NA | NA | NA | EC | Female |
| 2019 | 21 | Graham Newberry | GB | 61.33 | 22 | 127.29 | 17 | 188.62 | EC | Male |
| 2019 | 27 | Gerli Liinamäe | EE | 44.08 | 27 | NA | NA | NA | EC | Female |
| 2019 | 8 | Emmi Peltonen | FI | 58.06 | 10 | 111.97 | 8 | 170.03 | EC | Female |
| 2019 | 25 | Elzbieta Gabryszak | PL | 45.77 | 25 | NA | NA | NA | EC | Female |
| 2019 | 35 | Elizabete Jubkane | LV | 37.75 | 35 | NA | NA | NA | EC | Female |
| 2019 | 10 | Eliska Brezinova | CZ | 55.85 | 12 | 110.92 | 9 | 166.77 | EC | Female |
| 2019 | 12 | Ekaterina Ryabova | AZ | 59.95 | 7 | 103.22 | 13 | 163.17 | EC | Female |
| 2019 | 11 | Deniss Vasiljevs | LV | 78.87 | 12 | 140.63 | 10 | 219.50 | EC | Male |
| 2019 | 24 | Davidé Lewton Brain | MC | 61.07 | 23 | 112.54 | 23 | 173.61 | EC | Male |
| 2019 | 17 | Daša Grm | SI | 53.50 | 15 | 93.79 | 17 | 147.29 | EC | Female |
| 2019 | 13 | Daniel Samohin | IL | 86.48 | 6 | 130.69 | 14 | 217.17 | EC | Male |
| 2019 | 6 | Daniel Grassl | IT | 81.69 | 9 | 155.01 | 5 | 236.70 | EC | Male |
| 2019 | 33 | Conor Stakelum | IE | 55.03 | 33 | NA | NA | NA | EC | Male |
| 2019 | 33 | Camila Gjersem | NO | 39.81 | 33 | NA | NA | NA | EC | Female |
| 2019 | 28 | Burak Demirboga | TR | 56.95 | 28 | NA | NA | NA | EC | Male |
| 2019 | 24 | Antonina Dubinina | RS | 47.20 | 23 | 73.05 | 24 | 120.25 | EC | Female |
| 2019 | 18 | Anita Östlund | SE | 52.76 | 17 | 91.90 | 18 | 144.66 | EC | Female |
| 2019 | 22 | Anastasiya Galustyan | AM | 48.38 | 21 | 84.25 | 22 | 132.63 | EC | Female |
| 2019 | 36 | Anastasia Gozhva | UA | 35.51 | 36 | NA | NA | NA | EC | Female |
| 2019 | 2 | Alina Zagitova | RU | 75.00 | 1 | 123.34 | 4 | 198.34 | EC | Female |
| 2019 | 6 | Alexia Paganini | CH | 65.64 | 3 | 114.26 | 7 | 179.90 | EC | Female |
| 2019 | 9 | Alexei Bychenko | IL | 84.19 | 7 | 136.31 | 13 | 220.50 | EC | Male |
| 2019 | 11 | Alexandra Feigin | BG | 58.80 | 9 | 105.40 | 11 | 164.20 | EC | Female |
| 2019 | 2 | Alexander Samarin | RU | 91.97 | 2 | 177.87 | 2 | 269.84 | EC | Male |
| 2019 | 8 | Alexander Majorov | SE | 79.88 | 11 | 145.50 | 8 | 225.38 | EC | Male |
| 2019 | 37 | Alexander Borovoj | HU | 46.56 | 37 | NA | NA | NA | EC | Male |
| 2019 | 17 | Aleksandr Selevko | EE | 69.94 | 16 | 125.19 | 20 | 195.13 | EC | Male |
| 2019 | 12 | Adam Siao Him Fa | FR | 76.70 | 13 | 141.36 | 9 | 218.06 | EC | Male |
| 2019 | 11 | Yura Matsuda | JP | 53.35 | 10 | 104.24 | 10 | 157.59 | GPCAN | Female |
| 2019 | 6 | Wakaba Higuchi | JP | 66.51 | 2 | 114.78 | 7 | 181.29 | GPCAN | Female |
| 2019 | 7 | Starr Andrews | US | 64.77 | 4 | 109.95 | 9 | 174.72 | GPCAN | Female |
| 2019 | 1 | Shoma Uno | JP | 88.87 | 2 | 188.38 | 1 | 277.25 | GPCAN | Male |
| 2019 | 12 | Roman Sadovsky | CA | 67.72 | 12 | 142.88 | 8 | 210.60 | GPCAN | Male |
| 2019 | 5 | Nam Nguyen | CA | 82.22 | 7 | 158.72 | 5 | 240.94 | GPCAN | Male |
| 2019 | 4 | Mariah Bell | US | 63.35 | 5 | 126.90 | 4 | 190.25 | GPCAN | Female |
| 2019 | 2 | Mako Yamashita | JP | 66.30 | 3 | 136.76 | 2 | 203.06 | GPCAN | Female |
| 2019 | 7 | Kévin Aymoz | FR | 78.83 | 10 | 151.26 | 7 | 230.09 | GPCAN | Male |
| 2019 | 2 | Keegan Messing | CA | 95.05 | 1 | 170.12 | 2 | 265.17 | GPCAN | Male |
| 2019 | 9 | Kazuki Tomono | JP | 81.63 | 8 | 139.20 | 10 | 220.83 | GPCAN | Male |
| 2019 | 3 | Junhwan Cha | KR | 88.86 | 3 | 165.91 | 3 | 254.77 | GPCAN | Male |
| 2019 | 6 | Jason Brown | US | 76.46 | 11 | 158.51 | 6 | 234.97 | GPCAN | Male |
| 2019 | 3 | Evgenia Medvedeva | RU | 60.83 | 7 | 137.08 | 1 | 197.91 | GPCAN | Female |
| 2019 | 1 | Elizaveta Tuktamysheva | RU | 74.22 | 1 | 129.10 | 3 | 203.32 | GPCAN | Female |
| 2019 | 5 | Elizabet Tursynbayeva | KZ | 61.19 | 6 | 124.52 | 5 | 185.71 | GPCAN | Female |
| 2019 | 9 | Daria Panenkova | RU | 51.41 | 11 | 117.13 | 6 | 168.54 | GPCAN | Female |
| 2019 | 8 | Daniel Samohin | IL | 84.90 | 5 | 140.99 | 9 | 225.89 | GPCAN | Male |
| 2019 | 11 | Brendan Kerry | AU | 80.99 | 9 | 139.09 | 11 | 220.08 | GPCAN | Male |
| 2019 | 10 | Alicia Pineault | CA | 59.02 | 9 | 99.27 | 11 | 158.29 | GPCAN | Female |
| 2019 | 4 | Alexander Samarin | RU | 88.06 | 4 | 160.72 | 4 | 248.78 | GPCAN | Male |
| 2019 | 10 | Alexander Majorov | SE | 84.64 | 6 | 135.66 | 12 | 220.30 | GPCAN | Male |
| 2019 | 8 | Alaine Chartrand | CA | 60.47 | 8 | 111.70 | 8 | 172.17 | GPCAN | Female |
| 2019 | 5 | Sofia Samodurova | RU | 68.24 | 5 | 136.09 | 5 | 204.33 | GPF | Female |
| 2019 | 2 | Shoma Uno | JP | 91.67 | 2 | 183.43 | 2 | 275.10 | GPF | Male |
| 2019 | 6 | Sergei Voronov | RU | 82.96 | 5 | 143.48 | 6 | 226.44 | GPF | Male |
| 2019 | 6 | Satoko Miyahara | JP | 67.52 | 6 | 133.79 | 6 | 201.31 | GPF | Female |
| 2019 | 1 | Rika Kihira | JP | 82.51 | 1 | 150.61 | 1 | 233.12 | GPF | Female |
| 2019 | 1 | Nathan Chen | US | 92.99 | 1 | 189.43 | 1 | 282.42 | GPF | Male |
| 2019 | 4 | Michal Březina | CZ | 89.21 | 3 | 166.05 | 4 | 255.26 | GPF | Male |
| 2019 | 5 | Keegan Messing | CA | 79.56 | 6 | 156.49 | 5 | 236.05 | GPF | Male |
| 2019 | 4 | Kaori Sakamoto | JP | 70.23 | 4 | 141.45 | 4 | 211.68 | GPF | Female |
| 2019 | 3 | Junhwan Cha | KR | 89.07 | 4 | 174.42 | 3 | 263.49 | GPF | Male |
| 2019 | 3 | Elizaveta Tuktamysheva | RU | 70.65 | 3 | 144.67 | 3 | 215.32 | GPF | Female |
| 2019 | 2 | Alina Zagitova | RU | 77.93 | 2 | 148.60 | 2 | 226.53 | GPF | Female |
| 2019 | 1 | Yuzuru Hanyu | JP | 106.69 | 1 | 190.43 | 1 | 297.12 | GPFIN | Male |
| 2019 | 4 | Yuna Shiraiwa | JP | 63.77 | 2 | 127.69 | 5 | 191.46 | GPFIN | Female |
| 2019 | 8 | Viveca Lindfors | FI | 52.95 | 10 | 106.67 | 6 | 159.62 | GPFIN | Female |
| 2019 | 11 | Valtter Virtanen | FI | 48.16 | 11 | 106.58 | 11 | 154.74 | GPFIN | Male |
| 2019 | 2 | Stanislava Konstantinova | RU | 62.56 | 4 | 135.01 | 3 | 197.57 | GPFIN | Female |
| 2019 | 10 | Rika Hongo | JP | 51.11 | 11 | 105.48 | 7 | 156.59 | GPFIN | Female |
| 2019 | 10 | Phillip Harris | GB | 58.99 | 10 | 123.67 | 10 | 182.66 | GPFIN | Male |
| 2019 | 4 | Mikhail Kolyada | RU | 81.76 | 6 | 157.03 | 4 | 238.79 | GPFIN | Male |
| 2019 | 2 | Michal Březina | CZ | 93.31 | 2 | 164.67 | 2 | 257.98 | GPFIN | Male |
| 2019 | 5 | Loena Hendrickx | BE | 63.17 | 3 | 128.05 | 4 | 191.22 | GPFIN | Female |
| 2019 | 8 | Keiji Tanaka | JP | 80.60 | 7 | 126.22 | 9 | 206.82 | GPFIN | Male |
| 2019 | 3 | Kaori Sakamoto | JP | 57.26 | 7 | 140.16 | 2 | 197.42 | GPFIN | Female |
| 2019 | 3 | Junhwan Cha | KR | 82.82 | 4 | 160.37 | 3 | 243.19 | GPFIN | Male |
| 2019 | 7 | Hanul Kim | KR | 55.38 | 8 | 104.77 | 8 | 160.15 | GPFIN | Female |
| 2019 | 9 | Emmi Peltonen | FI | 59.90 | 5 | 98.82 | 10 | 158.72 | GPFIN | Female |
| 2019 | 6 | Daria Panenkova | RU | 58.23 | 6 | 103.25 | 9 | 161.48 | GPFIN | Female |
| 2019 | 5 | Boyang Jin | CN | 85.97 | 3 | 141.31 | 5 | 227.28 | GPFIN | Male |
| 2019 | 11 | Angela Wang | US | 53.76 | 9 | 95.81 | 11 | 149.57 | GPFIN | Female |
| 2019 | 6 | Andrei Lazukin | RU | 82.54 | 5 | 135.68 | 7 | 218.22 | GPFIN | Male |
| 2019 | 1 | Alina Zagitova | RU | 68.90 | 1 | 146.39 | 1 | 215.29 | GPFIN | Female |
| 2019 | 7 | Alexei Krasnozhon | US | 74.05 | 8 | 136.98 | 6 | 211.03 | GPFIN | Male |
| 2019 | 9 | Alexei Bychenko | IL | 73.44 | 9 | 128.89 | 8 | 202.33 | GPFIN | Male |
| 2019 | 6 | Romain Ponsart | FR | 84.97 | 4 | 144.89 | 6 | 229.86 | GPFRA | Male |
| 2019 | NA | Nicolas Nadeau | CA | 61.46 | 11 | NA | NA | NA | GPFRA | Male |
| 2019 | 1 | Nathan Chen | US | 86.94 | 3 | 184.64 | 1 | 271.58 | GPFRA | Male |
| 2019 | 5 | Kévin Aymoz | FR | 81.00 | 6 | 150.16 | 5 | 231.16 | GPFRA | Male |
| 2019 | 8 | Keiji Tanaka | JP | 79.35 | 8 | 136.97 | 8 | 216.32 | GPFRA | Male |
| 2019 | 2 | Jason Brown | US | 96.41 | 1 | 159.92 | 3 | 256.33 | GPFRA | Male |
| 2019 | 4 | Dmitri Aliev | RU | 75.15 | 9 | 162.67 | 2 | 237.82 | GPFRA | Male |
| 2019 | 7 | Deniss Vasiljevs | LV | 82.30 | 5 | 138.96 | 7 | 221.26 | GPFRA | Male |
| 2019 | 10 | Daniel Samohin | IL | 72.33 | 10 | 133.66 | 9 | 205.99 | GPFRA | Male |
| 2019 | 9 | Boyang Jin | CN | 79.41 | 7 | 129.48 | 10 | 208.89 | GPFRA | Male |
| 2019 | 3 | Alexander Samarin | RU | 90.86 | 2 | 156.23 | 4 | 247.09 | GPFRA | Male |
| 2019 | 12 | Yaroslav Paniot | UA | 68.59 | 9 | 105.05 | 12 | 173.64 | GPJPN | Male |
| 2019 | 4 | Vincent Zhou | US | 75.90 | 5 | 147.52 | 4 | 223.42 | GPJPN | Male |
| 2019 | 6 | Sota Yamamoto | JP | 74.98 | 6 | 138.42 | 5 | 213.40 | GPJPN | Male |
| 2019 | 1 | Shoma Uno | JP | 92.49 | 1 | 183.96 | 1 | 276.45 | GPJPN | Male |
| 2019 | 2 | Sergei Voronov | RU | 91.37 | 2 | 162.91 | 2 | 254.28 | GPJPN | Male |
| 2019 | 2 | Satoko Miyahara | JP | 76.08 | 2 | 143.39 | 2 | 219.47 | GPJPN | Female |
| 2019 | 1 | Rika Kihira | JP | 69.59 | 5 | 154.72 | 1 | 224.31 | GPJPN | Female |
| 2019 | 3 | Matteo Rizzo | IT | 77.00 | 4 | 147.71 | 3 | 224.71 | GPJPN | Male |
| 2019 | 5 | Mariah Bell | US | 62.97 | 7 | 135.99 | 4 | 198.96 | GPJPN | Female |
| 2019 | 9 | Maria Sotskova | RU | 60.75 | 9 | 116.24 | 9 | 176.99 | GPJPN | Female |
| 2019 | 4 | Mai Mihara | JP | 70.38 | 3 | 133.82 | 5 | 204.20 | GPJPN | Female |
| 2019 | 10 | Maé-Bérénice Méité | FR | 50.49 | 12 | 112.09 | 10 | 162.58 | GPJPN | Female |
| 2019 | 11 | Kevin Reynolds | CA | 61.14 | 12 | 121.53 | 10 | 182.67 | GPJPN | Male |
| 2019 | 12 | Kailani Craine | AU | 58.21 | 11 | 96.01 | 12 | 154.22 | GPJPN | Female |
| 2019 | 9 | Junehyoung Lee | KR | 66.16 | 11 | 122.10 | 9 | 188.26 | GPJPN | Male |
| 2019 | 10 | Hiroaki Sato | JP | 67.38 | 10 | 117.80 | 11 | 185.18 | GPJPN | Male |
| 2019 | 6 | Eunsoo Lim | KR | 69.78 | 4 | 126.53 | 6 | 196.31 | GPJPN | Female |
| 2019 | 3 | Elizaveta Tuktamysheva | RU | 76.17 | 1 | 142.85 | 3 | 219.02 | GPJPN | Female |
| 2019 | 5 | Dmitri Aliev | RU | 81.16 | 3 | 138.36 | 6 | 219.52 | GPJPN | Male |
| 2019 | 8 | Deniss Vasiljevs | LV | 72.39 | 7 | 125.21 | 8 | 197.60 | GPJPN | Male |
| 2019 | 8 | Courtney Hicks | US | 59.10 | 10 | 118.97 | 8 | 178.07 | GPJPN | Female |
| 2019 | 11 | Angela Wang | US | 60.82 | 8 | 98.54 | 11 | 159.36 | GPJPN | Female |
| 2019 | 7 | Alexander Johnson | US | 72.03 | 8 | 127.72 | 7 | 199.75 | GPJPN | Male |
| 2019 | 7 | Alena Leonova | RU | 68.22 | 6 | 125.93 | 7 | 194.15 | GPJPN | Female |
| 2019 | 1 | Yuzuru Hanyu | JP | 110.53 | 1 | 167.89 | 1 | 278.42 | GPRUS | Male |
| 2019 | 9 | Yura Matsuda | JP | 52.00 | 8 | 85.99 | 9 | 137.99 | GPRUS | Female |
| 2019 | 5 | Yuna Shiraiwa | JP | 60.35 | 5 | 120.58 | 4 | 180.93 | GPRUS | Female |
| 2019 | 2 | Sofia Samodurova | RU | 67.40 | 2 | 130.61 | 2 | 198.01 | GPRUS | Female |
| 2019 | 8 | Polina Tsurskaya | RU | 56.81 | 7 | 92.64 | 8 | 149.45 | GPRUS | Female |
| 2019 | 6 | Paul Fentz | DE | 78.28 | 5 | 142.29 | 7 | 220.57 | GPRUS | Male |
| 2019 | 2 | Morisi Kvitelashvili | GE | 89.94 | 2 | 158.64 | 2 | 248.58 | GPRUS | Male |
| 2019 | 4 | Mikhail Kolyada | RU | 69.10 | 8 | 156.32 | 4 | 225.42 | GPRUS | Male |
| 2019 | 7 | Mako Yamashita | JP | 51.00 | 9 | 110.22 | 7 | 161.22 | GPRUS | Female |
| 2019 | 5 | Keegan Messing | CA | 73.83 | 7 | 146.92 | 6 | 220.75 | GPRUS | Male |
| 2019 | 3 | Kazuki Tomono | JP | 82.26 | 4 | 156.47 | 3 | 238.73 | GPRUS | Male |
| 2019 | 12 | Julian Zhi-Jie Yee | MY | 60.37 | 12 | 118.34 | 12 | 178.71 | GPRUS | Male |
| 2019 | NA | Gracie Gold | US | 37.51 | 10 | NA | NA | NA | GPRUS | Female |
| 2019 | 3 | Eunsoo Lim | KR | 57.76 | 6 | 127.91 | 3 | 185.67 | GPRUS | Female |
| 2019 | 6 | Elizabet Tursynbayeva | KZ | 61.73 | 4 | 118.72 | 6 | 180.45 | GPRUS | Female |
| 2019 | 10 | Brendan Kerry | AU | 65.22 | 10 | 132.37 | 9 | 197.59 | GPRUS | Male |
| 2019 | 11 | Artur Dmitriev | RU | 67.58 | 9 | 122.00 | 11 | 189.58 | GPRUS | Male |
| 2019 | 7 | Andrei Lazukin | RU | 62.45 | 11 | 153.33 | 5 | 215.78 | GPRUS | Male |
| 2019 | 1 | Alina Zagitova | RU | 80.78 | 1 | 142.17 | 1 | 222.95 | GPRUS | Female |
| 2019 | 4 | Alexia Paganini | CH | 63.43 | 3 | 119.07 | 5 | 182.50 | GPRUS | Female |
| 2019 | 8 | Alexei Krasnozhon | US | 75.32 | 6 | 132.69 | 8 | 208.01 | GPRUS | Male |
| 2019 | 9 | Alexander Majorov | SE | 82.33 | 3 | 123.26 | 10 | 205.59 | GPRUS | Male |
| 2019 | 5 | Vincent Zhou | US | 76.38 | 6 | 149.37 | 3 | 225.75 | GPUSA | Male |
| 2019 | 10 | Starr Andrews | US | 56.03 | 9 | 94.53 | 10 | 150.56 | GPUSA | Female |
| 2019 | 3 | Sofia Samodurova | RU | 64.41 | 3 | 134.29 | 3 | 198.70 | GPUSA | Female |
| 2019 | 3 | Sergei Voronov | RU | 78.18 | 4 | 148.26 | 4 | 226.44 | GPUSA | Male |
| 2019 | 1 | Satoko Miyahara | JP | 73.86 | 1 | 145.85 | 1 | 219.71 | GPUSA | Female |
| 2019 | 10 | Romain Ponsart | FR | 71.48 | 8 | 116.44 | 11 | 187.92 | GPUSA | Male |
| 2019 | 7 | Polina Tsurskaya | RU | 58.42 | 8 | 101.03 | 8 | 159.45 | GPUSA | Female |
| 2019 | 1 | Nathan Chen | US | 90.58 | 1 | 189.99 | 1 | 280.57 | GPUSA | Male |
| 2019 | 6 | Nam Nguyen | CA | 69.86 | 9 | 143.13 | 6 | 212.99 | GPUSA | Male |
| 2019 | 8 | Morisi Kvitelashvili | GE | 68.58 | 11 | 136.54 | 7 | 205.12 | GPUSA | Male |
| 2019 | 2 | Michal Březina | CZ | 82.09 | 2 | 157.42 | 2 | 239.51 | GPUSA | Male |
| 2019 | 6 | Megan Wessenberg | US | 60.20 | 6 | 110.13 | 6 | 170.33 | GPUSA | Female |
| 2019 | 4 | Matteo Rizzo | IT | 78.09 | 5 | 147.72 | 5 | 225.81 | GPUSA | Male |
| 2019 | 8 | Marin Honda | JP | 62.74 | 4 | 95.30 | 9 | 158.04 | GPUSA | Female |
| 2019 | NA | Loena Hendrickx | BE | 54.13 | 10 | NA | NA | NA | GPUSA | Female |
| 2019 | 5 | Laurine Lecavelier | FR | 59.57 | 7 | 112.84 | 5 | 172.41 | GPUSA | Female |
| 2019 | 11 | Kevin Reynolds | CA | 61.62 | 12 | 124.01 | 10 | 185.63 | GPUSA | Male |
| 2019 | 2 | Kaori Sakamoto | JP | 71.29 | 2 | 142.61 | 2 | 213.90 | GPUSA | Female |
| 2019 | 7 | Julian Zhi-Jie Yee | MY | 81.52 | 3 | 125.99 | 9 | 207.51 | GPUSA | Male |
| 2019 | 12 | Jimmy Ma | US | 71.53 | 7 | 113.53 | 12 | 185.06 | GPUSA | Male |
| 2019 | 4 | Bradie Tennell | US | 61.72 | 5 | 131.17 | 4 | 192.89 | GPUSA | Female |
| 2019 | 9 | Alexei Bychenko | IL | 69.69 | 10 | 127.78 | 8 | 197.47 | GPUSA | Male |
| 2019 | 9 | Alaine Chartrand | CA | 46.99 | 11 | 108.50 | 7 | 155.49 | GPUSA | Female |
| 2019 | 5 | Stanislava Konstantinova | RU | 54.91 | 10 | 134.76 | 4 | 189.67 | GPFRA | Female |
| 2019 | 1 | Rika Kihira | JP | 67.64 | 2 | 138.28 | 1 | 205.92 | GPFRA | Female |
| 2019 | 12 | Matilda Algotsson | SE | 48.58 | 12 | 97.77 | 11 | 146.35 | GPFRA | Female |
| 2019 | 6 | Marin Honda | JP | 65.37 | 4 | 123.24 | 6 | 188.61 | GPFRA | Female |
| 2019 | 7 | Maria Sotskova | RU | 61.76 | 5 | 115.83 | 7 | 177.59 | GPFRA | Female |
| 2019 | 2 | Mai Mihara | JP | 67.95 | 1 | 134.86 | 3 | 202.81 | GPFRA | Female |
| 2019 | 8 | Maé-Bérénice Méité | FR | 60.86 | 7 | 107.16 | 8 | 168.02 | GPFRA | Female |
| 2019 | 11 | Léa Serna | FR | 55.31 | 9 | 94.18 | 12 | 149.49 | GPFRA | Female |
| 2019 | 9 | Laurine Lecavelier | FR | 51.66 | 11 | 105.58 | 9 | 157.24 | GPFRA | Female |
| 2019 | 4 | Evgenia Medvedeva | RU | 67.55 | 3 | 125.26 | 5 | 192.81 | GPFRA | Female |
| 2019 | 3 | Bradie Tennell | US | 61.34 | 6 | 136.44 | 2 | 197.78 | GPFRA | Female |
| 2019 | 10 | Alexia Paganini | CH | 56.88 | 8 | 99.63 | 10 | 156.51 | GPFRA | Female |
| 2018 | 1 | Kaori Sakamoto | JP | 71.34 | 2 | 142.87 | 1 | 214.21 | 4CC | Female |
| 2018 | 2 | Mai Mihara | JP | 69.84 | 3 | 140.73 | 2 | 210.57 | 4CC | Female |
| 2018 | 3 | Satoko Miyahara | JP | 71.74 | 1 | 135.28 | 3 | 207.02 | 4CC | Female |
| 2018 | 4 | Dabin Choi | KR | 62.30 | 5 | 127.93 | 4 | 190.23 | 4CC | Female |
| 2018 | 5 | Mariah Bell | US | 62.90 | 4 | 122.94 | 5 | 185.84 | 4CC | Female |
| 2018 | 6 | Hanul Kim | KR | 61.15 | 6 | 111.95 | 8 | 173.10 | 4CC | Female |
| 2018 | 7 | Starr Andrews | US | 60.61 | 7 | 112.04 | 7 | 172.65 | 4CC | Female |
| 2018 | 8 | Alaine Chartrand | CA | 59.86 | 8 | 112.55 | 6 | 172.41 | 4CC | Female |
| 2018 | 9 | Angela Wang | US | 58.97 | 9 | 102.07 | 11 | 161.04 | 4CC | Female |
| 2018 | 10 | Xiangning Li | CN | 57.01 | 10 | 103.39 | 10 | 160.40 | 4CC | Female |
| 2018 | 11 | Soyoun Park | KR | 53.05 | 12 | 106.43 | 9 | 159.48 | 4CC | Female |
| 2018 | 12 | Elizabet Tursynbayeva | KZ | 56.52 | 11 | 99.67 | 13 | 156.19 | 4CC | Female |
| 2018 | 13 | Alicia Pineault | CA | 51.53 | 14 | 101.28 | 12 | 152.81 | 4CC | Female |
| 2018 | 14 | Brooklee Han | AU | 52.29 | 13 | 98.36 | 14 | 150.65 | 4CC | Female |
| 2018 | 15 | Michelle Long | CA | 49.77 | 17 | 97.73 | 15 | 147.50 | 4CC | Female |
| 2018 | 16 | Kailani Craine | AU | 50.79 | 16 | 90.25 | 16 | 141.04 | 4CC | Female |
| 2018 | 17 | Ziquan Zhao | CN | 48.78 | 18 | 90.23 | 17 | 139.01 | 4CC | Female |
| 2018 | 18 | Amy Lin | TW | 51.14 | 15 | 86.26 | 18 | 137.40 | 4CC | Female |
| 2018 | 19 | Chloe Ing | SG | 45.30 | 19 | 84.40 | 20 | 129.70 | 4CC | Female |
| 2018 | 20 | Aiza Imambek | KZ | 36.47 | 20 | 84.53 | 19 | 121.00 | 4CC | Female |
| 2018 | 21 | Joanna So | HK | 35.51 | 21 | 72.18 | 21 | 107.69 | 4CC | Female |
| 2018 | 22 | Natalie Pailin Sangkagalo | TH | 33.50 | 22 | 60.11 | 22 | 93.61 | 4CC | Female |
| 2018 | 23 | Thita Lamsam | TH | 28.26 | 23 | 51.07 | 23 | 79.33 | 4CC | Female |
| 2018 | 1 | Boyang Jin | CN | 100.17 | 2 | 200.78 | 1 | 300.95 | 4CC | Male |
| 2018 | 2 | Shoma Uno | JP | 100.49 | 1 | 197.45 | 2 | 297.94 | 4CC | Male |
| 2018 | 3 | Jason Brown | US | 89.78 | 4 | 179.44 | 3 | 269.22 | 4CC | Male |
| 2018 | 4 | Keiji Tanaka | JP | 90.68 | 3 | 169.63 | 5 | 260.31 | 4CC | Male |
| 2018 | 5 | Max Aaron | US | 84.15 | 6 | 171.30 | 4 | 255.45 | 4CC | Male |
| 2018 | 6 | Misha Ge | UZ | 82.27 | 8 | 166.69 | 7 | 248.96 | 4CC | Male |
| 2018 | 7 | Kevin Reynolds | CA | 74.65 | 13 | 166.85 | 6 | 241.50 | 4CC | Male |
| 2018 | 8 | Elladj Baldé | CA | 75.17 | 12 | 163.03 | 8 | 238.20 | 4CC | Male |
| 2018 | 9 | Nam Nguyen | CA | 84.09 | 7 | 153.43 | 10 | 237.52 | 4CC | Male |
| 2018 | 10 | Han Yan | CN | 84.74 | 5 | 143.19 | 12 | 227.93 | 4CC | Male |
| 2018 | 11 | Grant Hochstein | US | 70.80 | 15 | 155.59 | 9 | 226.39 | 4CC | Male |
| 2018 | 12 | Takahito Mura | JP | 76.66 | 10 | 148.75 | 11 | 225.41 | 4CC | Male |
| 2018 | 13 | Brendan Kerry | AU | 79.57 | 9 | 140.38 | 14 | 219.95 | 4CC | Male |
| 2018 | 14 | Junehyoung Lee | KR | 69.93 | 16 | 141.93 | 13 | 211.86 | 4CC | Male |
| 2018 | 15 | Denis Ten | KZ | 75.30 | 11 | 135.52 | 15 | 210.82 | 4CC | Male |
| 2018 | 16 | Julian Zhi-Jie Yee | MY | 68.45 | 17 | 129.23 | 16 | 197.68 | 4CC | Male |
| 2018 | 17 | Chih-I Tsao | TW | 72.57 | 14 | 122.64 | 19 | 195.21 | 4CC | Male |
| 2018 | 18 | Donovan Carrillo | MX | 59.07 | 22 | 126.84 | 17 | 185.91 | 4CC | Male |
| 2018 | 19 | He Zhang | CN | 63.62 | 19 | 121.20 | 20 | 184.82 | 4CC | Male |
| 2018 | 20 | Geonhyeong An | KR | 56.67 | 23 | 123.59 | 18 | 180.26 | 4CC | Male |
| 2018 | 21 | Andrew Dodds | AU | 63.69 | 18 | 114.12 | 23 | 177.81 | 4CC | Male |
| 2018 | 22 | Sihyeong Lee | KR | 62.65 | 20 | 114.42 | 22 | 177.07 | 4CC | Male |
| 2018 | 23 | Abzal Rakimgaliev | KZ | 60.77 | 21 | 114.81 | 21 | 175.58 | 4CC | Male |
| 2018 | 24 | Leslie Man Cheuk Ip | HK | 53.80 | 24 | 96.43 | 24 | 150.23 | 4CC | Male |
| 2018 | 25 | Micah Kai Lynette | TH | 53.29 | 25 | NA | NA | NA | 4CC | Male |
| 2018 | 26 | Harrison Jon Yen Wong | HK | 52.78 | 26 | NA | NA | NA | 4CC | Male |
| 2018 | 27 | Kai Xiang Chew | MY | 50.92 | 27 | NA | NA | NA | 4CC | Male |
| 2018 | 28 | Mark Webster | AU | 49.45 | 28 | NA | NA | NA | 4CC | Male |
| 2018 | 29 | Harry Hau Yin Lee | HK | 43.98 | 29 | NA | NA | NA | 4CC | Male |
| 2018 | 30 | Micah Tang | TW | 43.05 | 30 | NA | NA | NA | 4CC | Male |
| 2018 | 1 | Alina Zagitova | RU | 80.27 | 1 | 157.97 | 1 | 238.24 | EC | Female |
| 2018 | 2 | Evgenia Medvedeva | RU | 78.57 | 2 | 154.29 | 2 | 232.86 | EC | Female |
| 2018 | 3 | Carolina Kostner | IT | 78.30 | 3 | 125.95 | 4 | 204.25 | EC | Female |
| 2018 | 4 | Maria Sotskova | RU | 68.70 | 4 | 132.11 | 3 | 200.81 | EC | Female |
| 2018 | 5 | Loena Hendrickx | BE | 55.13 | 8 | 121.78 | 5 | 176.91 | EC | Female |
| 2018 | 6 | Nicole Rajičová | SK | 61.01 | 5 | 110.89 | 6 | 171.90 | EC | Female |
| 2018 | 7 | Alexia Paganini | CH | 54.95 | 9 | 106.67 | 9 | 161.62 | EC | Female |
| 2018 | 8 | Maé-Bérénice Méité | FR | 54.14 | 10 | 105.56 | 10 | 159.70 | EC | Female |
| 2018 | 9 | Emmi Peltonen | FI | 52.68 | 11 | 106.80 | 8 | 159.48 | EC | Female |
| 2018 | 10 | Nicole Schott | DE | 48.37 | 18 | 109.47 | 7 | 157.84 | EC | Female |
| 2018 | 11 | Laurine Lecavelier | FR | 55.36 | 7 | 98.75 | 12 | 154.11 | EC | Female |
| 2018 | 12 | Eliska Brezinova | CZ | 52.06 | 12 | 97.63 | 14 | 149.69 | EC | Female |
| 2018 | 13 | Ivett Tóth | HU | 50.70 | 15 | 98.28 | 13 | 148.98 | EC | Female |
| 2018 | 14 | Viveca Lindfors | FI | 51.62 | 14 | 96.27 | 17 | 147.89 | EC | Female |
| 2018 | 15 | Micol Cristini | IT | 48.22 | 19 | 99.58 | 11 | 147.80 | EC | Female |
| 2018 | 16 | Lea Johanna Dastich | DE | 49.89 | 16 | 96.93 | 15 | 146.82 | EC | Female |
| 2018 | 17 | Anita Östlund | SE | 56.04 | 6 | 89.10 | 20 | 145.14 | EC | Female |
| 2018 | 18 | Anne Line Gjersem | NO | 48.70 | 17 | 93.98 | 18 | 142.68 | EC | Female |
| 2018 | 19 | Giada Russo | IT | 45.81 | 23 | 96.57 | 16 | 142.38 | EC | Female |
| 2018 | 20 | Daša Grm | SI | 47.40 | 20 | 89.91 | 19 | 137.31 | EC | Female |
| 2018 | 21 | Pernille Sørensen | DK | 45.76 | 24 | 88.18 | 21 | 133.94 | EC | Female |
| 2018 | 22 | Elzbieta Kropa | LT | 46.06 | 21 | 87.81 | 22 | 133.87 | EC | Female |
| 2018 | 23 | Anna Khnychenkova | UA | 51.84 | 13 | 80.86 | 23 | 132.70 | EC | Female |
| 2018 | 24 | Silvia Hugec | SK | 45.98 | 22 | 77.47 | 24 | 123.45 | EC | Female |
| 2018 | 25 | Kristina Lisovskaja | EE | 45.74 | 25 | NA | NA | NA | EC | Female |
| 2018 | 26 | Kyarha Van Tiel | NL | 45.28 | 26 | NA | NA | NA | EC | Female |
| 2018 | 27 | Natasha Mckay | GB | 45.12 | 27 | NA | NA | NA | EC | Female |
| 2018 | 28 | Fruzsina Medgyesi | HU | 44.71 | 28 | NA | NA | NA | EC | Female |
| 2018 | 29 | Julia Sauter | RO | 44.57 | 29 | NA | NA | NA | EC | Female |
| 2018 | 30 | Natalie Klotz | AT | 43.53 | 30 | NA | NA | NA | EC | Female |
| 2018 | 31 | Matilda Algotsson | SE | 43.28 | 31 | NA | NA | NA | EC | Female |
| 2018 | 32 | Sila Saygi | TR | 43.05 | 32 | NA | NA | NA | EC | Female |
| 2018 | 33 | Valentina Matos | ES | 39.66 | 33 | NA | NA | NA | EC | Female |
| 2018 | 34 | Elzbieta Gabryszak | PL | 38.00 | 34 | NA | NA | NA | EC | Female |
| 2018 | 35 | Kim Cheremsky | AZ | 37.61 | 35 | NA | NA | NA | EC | Female |
| 2018 | 36 | Diana Nikitina | LV | 36.71 | 36 | NA | NA | NA | EC | Female |
| 2018 | 37 | Antonina Dubinina | RS | 36.69 | 37 | NA | NA | NA | EC | Female |
| 2018 | 38 | Aimee Buchanan | IL | 33.87 | 38 | NA | NA | NA | EC | Female |
| 2018 | 39 | Presiyana Dimitrova | BG | 24.76 | 39 | NA | NA | NA | EC | Female |
| 2018 | 1 | Javier Fernández | ES | 103.82 | 1 | 191.73 | 1 | 295.55 | EC | Male |
| 2018 | 2 | Dmitri Aliev | RU | 91.33 | 2 | 182.73 | 2 | 274.06 | EC | Male |
| 2018 | 3 | Mikhail Kolyada | RU | 83.41 | 4 | 175.49 | 3 | 258.90 | EC | Male |
| 2018 | 4 | Deniss Vasiljevs | LV | 85.11 | 3 | 158.41 | 5 | 243.52 | EC | Male |
| 2018 | 5 | Alexei Bychenko | IL | 74.97 | 8 | 163.47 | 4 | 238.44 | EC | Male |
| 2018 | 6 | Alexander Samarin | RU | 74.25 | 9 | 155.56 | 6 | 229.81 | EC | Male |
| 2018 | 7 | Alexander Majorov | SE | 71.28 | 12 | 154.58 | 7 | 225.86 | EC | Male |
| 2018 | 8 | Michal Březina | CZ | 72.72 | 10 | 152.48 | 8 | 225.20 | EC | Male |
| 2018 | 9 | Matteo Rizzo | IT | 78.26 | 6 | 141.17 | 9 | 219.43 | EC | Male |
| 2018 | 10 | Jorik Hendrickx | BE | 78.56 | 5 | 139.61 | 12 | 218.17 | EC | Male |
| 2018 | 11 | Chafik Besseghier | FR | 70.35 | 13 | 140.82 | 10 | 211.17 | EC | Male |
| 2018 | 12 | Morisi Kvitelashvili | GE | 76.74 | 7 | 133.73 | 14 | 210.47 | EC | Male |
| 2018 | 13 | Phillip Harris | GB | 67.77 | 15 | 140.45 | 11 | 208.22 | EC | Male |
| 2018 | 14 | Romain Ponsart | FR | 61.45 | 20 | 139.27 | 13 | 200.72 | EC | Male |
| 2018 | 15 | Slavik Hayrapetyan | AM | 69.49 | 14 | 127.14 | 16 | 196.63 | EC | Male |
| 2018 | 16 | Paul Fentz | DE | 72.54 | 11 | 122.43 | 17 | 194.97 | EC | Male |
| 2018 | 17 | Irakli Maysuradze | GE | 63.69 | 18 | 127.53 | 15 | 191.22 | EC | Male |
| 2018 | 18 | Stéphane Walker | CH | 65.96 | 16 | 119.45 | 20 | 185.41 | EC | Male |
| 2018 | 19 | Valtter Virtanen | FI | 60.23 | 24 | 121.54 | 18 | 181.77 | EC | Male |
| 2018 | 20 | Felipe Montoya Pulgarín | ES | 61.23 | 22 | 120.49 | 19 | 181.72 | EC | Male |
| 2018 | 21 | Daniel Albert Naurits | EE | 60.76 | 23 | 115.34 | 21 | 176.10 | EC | Male |
| 2018 | 22 | Sondre Oddvoll Bøe | NO | 61.85 | 19 | 108.79 | 22 | 170.64 | EC | Male |
| 2018 | 23 | Burak Demirboga | TR | 61.27 | 21 | 105.95 | 23 | 167.22 | EC | Male |
| 2018 | 24 | Ihor Reznichenko | PL | 63.96 | 17 | 101.69 | 24 | 165.65 | EC | Male |
| 2018 | 25 | Yaroslav Paniot | UA | 60.07 | 25 | NA | NA | NA | EC | Male |
| 2018 | 26 | Daniel Samohin | IL | 59.18 | 26 | NA | NA | NA | EC | Male |
| 2018 | 27 | Nicholas Vrdoljak | HR | 58.30 | 27 | NA | NA | NA | EC | Male |
| 2018 | 28 | Jiri Belohradsky | CZ | 58.30 | 28 | NA | NA | NA | EC | Male |
| 2018 | 29 | Michael Neuman | SK | 57.44 | 29 | NA | NA | NA | EC | Male |
| 2018 | 30 | Thomas Kennes | NL | 54.65 | 30 | NA | NA | NA | EC | Male |
| 2018 | 31 | Davidé Lewton Brain | MC | 54.64 | 31 | NA | NA | NA | EC | Male |
| 2018 | 32 | Larry Loupolover | AZ | 52.44 | 32 | NA | NA | NA | EC | Male |
| 2018 | 33 | Alexander Maszljanko | HU | 52.01 | 33 | NA | NA | NA | EC | Male |
| 2018 | 34 | Nicky-Leo Obreykov | BG | 50.24 | 34 | NA | NA | NA | EC | Male |
| 2018 | 35 | Yakau Zenko | BY | 47.26 | 35 | NA | NA | NA | EC | Male |
| 2018 | 36 | Conor Stakelum | IE | 43.05 | 36 | NA | NA | NA | EC | Male |
| 2018 | 1 | Kaetlyn Osmond | CA | 76.06 | 1 | 136.85 | 1 | 212.91 | GPCAN | Female |
| 2018 | 2 | Maria Sotskova | RU | 66.10 | 3 | 126.42 | 2 | 192.52 | GPCAN | Female |
| 2018 | 3 | Ashley Wagner | US | 61.57 | 7 | 122.37 | 4 | 183.94 | GPCAN | Female |
| 2018 | 4 | Courtney Hicks | US | 64.06 | 4 | 118.51 | 5 | 182.57 | GPCAN | Female |
| 2018 | 5 | Marin Honda | JP | 52.60 | 10 | 125.64 | 3 | 178.24 | GPCAN | Female |
| 2018 | 6 | Rika Hongo | JP | 61.60 | 6 | 114.74 | 6 | 176.34 | GPCAN | Female |
| 2018 | 7 | Karen Chen | US | 61.77 | 5 | 108.63 | 7 | 170.40 | GPCAN | Female |
| 2018 | 8 | Laurine Lecavelier | FR | 59.08 | 8 | 107.35 | 8 | 166.43 | GPCAN | Female |
| 2018 | 9 | Anna Pogorilaya | RU | 69.05 | 2 | 87.84 | 10 | 156.89 | GPCAN | Female |
| 2018 | 10 | Kailani Craine | AU | 54.96 | 9 | 88.07 | 9 | 143.03 | GPCAN | Female |
| 2018 | 11 | Alaine Chartrand | CA | 46.51 | 11 | 87.66 | 11 | 134.17 | GPCAN | Female |
| 2018 | 12 | Larkyn Austman | CA | 41.79 | 12 | 81.77 | 12 | 123.56 | GPCAN | Female |
| 2018 | 1 | Shoma Uno | JP | 103.62 | 1 | 197.48 | 1 | 301.10 | GPCAN | Male |
| 2018 | 2 | Jason Brown | US | 90.71 | 3 | 170.43 | 2 | 261.14 | GPCAN | Male |
| 2018 | 3 | Alexander Samarin | RU | 84.02 | 4 | 166.04 | 3 | 250.06 | GPCAN | Male |
| 2018 | 4 | Patrick Chan | CA | 94.43 | 2 | 151.27 | 7 | 245.70 | GPCAN | Male |
| 2018 | 5 | Jorik Hendrickx | BE | 82.08 | 6 | 155.23 | 5 | 237.31 | GPCAN | Male |
| 2018 | 6 | Michal Březina | CZ | 80.34 | 7 | 156.70 | 4 | 237.04 | GPCAN | Male |
| 2018 | 7 | Nicolas Nadeau | CA | 74.23 | 9 | 155.20 | 6 | 229.43 | GPCAN | Male |
| 2018 | 8 | Keegan Messing | CA | 82.17 | 5 | 135.58 | 10 | 217.75 | GPCAN | Male |
| 2018 | 9 | Junhwan Cha | KR | 68.46 | 11 | 141.86 | 8 | 210.32 | GPCAN | Male |
| 2018 | 10 | Paul Fentz | DE | 68.48 | 10 | 133.12 | 11 | 201.60 | GPCAN | Male |
| 2018 | 11 | Brendan Kerry | AU | 63.19 | 12 | 138.37 | 9 | 201.56 | GPCAN | Male |
| 2018 | 12 | Takahito Mura | JP | 74.82 | 8 | 111.84 | 12 | 186.66 | GPCAN | Male |
| 2018 | 1 | Alina Zagitova | RU | 69.44 | 4 | 144.44 | 1 | 213.88 | GPCHN | Female |
| 2018 | 2 | Wakaba Higuchi | JP | 70.53 | 2 | 141.99 | 2 | 212.52 | GPCHN | Female |
| 2018 | 3 | Elena Radionova | RU | 70.48 | 3 | 136.34 | 4 | 206.82 | GPCHN | Female |
| 2018 | 4 | Mai Mihara | JP | 66.90 | 7 | 139.17 | 3 | 206.07 | GPCHN | Female |
| 2018 | 5 | Marin Honda | JP | 66.90 | 6 | 131.42 | 5 | 198.32 | GPCHN | Female |
| 2018 | 6 | Gabrielle Daleman | CA | 70.65 | 1 | 126.18 | 7 | 196.83 | GPCHN | Female |
| 2018 | 7 | Elizaveta Tuktamysheva | RU | 67.10 | 5 | 129.58 | 6 | 196.68 | GPCHN | Female |
| 2018 | 8 | Xiangning Li | CN | 59.20 | 8 | 115.62 | 8 | 174.82 | GPCHN | Female |
| 2018 | 9 | Dabin Choi | KR | 53.90 | 9 | 112.09 | 9 | 165.99 | GPCHN | Female |
| 2018 | 10 | Amber Glenn | US | 52.61 | 10 | 98.53 | 10 | 151.14 | GPCHN | Female |
| 2018 | 11 | Ziquan Zhao | CN | 50.39 | 11 | 94.32 | 11 | 144.71 | GPCHN | Female |
| 2018 | 1 | Mikhail Kolyada | RU | 103.13 | 1 | 176.25 | 3 | 279.38 | GPCHN | Male |
| 2018 | 2 | Boyang Jin | CN | 93.89 | 2 | 170.59 | 5 | 264.48 | GPCHN | Male |
| 2018 | 3 | Max Aaron | US | 83.11 | 5 | 176.58 | 1 | 259.69 | GPCHN | Male |
| 2018 | 4 | Vincent Zhou | US | 80.23 | 8 | 176.43 | 2 | 256.66 | GPCHN | Male |
| 2018 | 5 | Han Yan | CN | 82.22 | 6 | 172.39 | 4 | 254.61 | GPCHN | Male |
| 2018 | 6 | Javier Fernández | ES | 90.57 | 3 | 162.49 | 6 | 253.06 | GPCHN | Male |
| 2018 | 7 | Keiji Tanaka | JP | 87.19 | 4 | 159.98 | 8 | 247.17 | GPCHN | Male |
| 2018 | 8 | Kevin Reynolds | CA | 64.40 | 10 | 162.10 | 7 | 226.50 | GPCHN | Male |
| 2018 | 9 | Grant Hochstein | US | 80.55 | 7 | 135.89 | 9 | 216.44 | GPCHN | Male |
| 2018 | 10 | Alexander Majorov | SE | 64.27 | 11 | 121.77 | 10 | 186.04 | GPCHN | Male |
| 2018 | 11 | Alexander Petrov | RU | 68.58 | 9 | 117.44 | 12 | 186.02 | GPCHN | Male |
| 2018 | 12 | He Zhang | CN | 46.99 | 12 | 120.59 | 11 | 167.58 | GPCHN | Male |
| 2018 | 1 | Alina Zagitova | RU | 76.27 | 2 | 147.03 | 1 | 223.30 | GPF | Female |
| 2018 | 2 | Maria Sotskova | RU | 74.00 | 4 | 142.28 | 2 | 216.28 | GPF | Female |
| 2018 | 3 | Kaetlyn Osmond | CA | 77.04 | 1 | 138.12 | 5 | 215.16 | GPF | Female |
| 2018 | 4 | Carolina Kostner | IT | 72.82 | 6 | 141.83 | 3 | 214.65 | GPF | Female |
| 2018 | 5 | Satoko Miyahara | JP | 74.61 | 3 | 138.88 | 4 | 213.49 | GPF | Female |
| 2018 | 6 | Wakaba Higuchi | JP | 73.26 | 5 | 128.85 | 6 | 202.11 | GPF | Female |
| 2018 | 1 | Nathan Chen | US | 103.32 | 1 | 183.19 | 2 | 286.51 | GPF | Male |
| 2018 | 2 | Shoma Uno | JP | 101.51 | 2 | 184.50 | 1 | 286.01 | GPF | Male |
| 2018 | 3 | Mikhail Kolyada | RU | 99.22 | 3 | 182.78 | 3 | 282.00 | GPF | Male |
| 2018 | 4 | Sergei Voronov | RU | 87.77 | 5 | 178.82 | 4 | 266.59 | GPF | Male |
| 2018 | 5 | Adam Rippon | US | 86.19 | 6 | 168.14 | 5 | 254.33 | GPF | Male |
| 2018 | 6 | Jason Brown | US | 89.02 | 4 | 164.79 | 6 | 253.81 | GPF | Male |
| 2018 | 1 | Alina Zagitova | RU | 62.46 | 5 | 151.34 | 1 | 213.80 | GPFRA | Female |
| 2018 | 2 | Maria Sotskova | RU | 67.79 | 2 | 140.99 | 2 | 208.78 | GPFRA | Female |
| 2018 | 3 | Kaetlyn Osmond | CA | 69.05 | 1 | 137.72 | 4 | 206.77 | GPFRA | Female |
| 2018 | 4 | Mai Mihara | JP | 64.57 | 4 | 137.55 | 5 | 202.12 | GPFRA | Female |
| 2018 | 5 | Elizabet Tursynbayeva | KZ | 62.29 | 6 | 138.69 | 3 | 200.98 | GPFRA | Female |
| 2018 | 6 | Yuna Shiraiwa | JP | 66.05 | 3 | 127.13 | 6 | 193.18 | GPFRA | Female |
| 2018 | 7 | Nicole Schott | DE | 55.54 | 10 | 116.85 | 7 | 172.39 | GPFRA | Female |
| 2018 | 8 | Maé-Bérénice Méité | FR | 58.96 | 8 | 112.44 | 9 | 171.40 | GPFRA | Female |
| 2018 | 9 | Elizaveta Tuktamysheva | RU | 53.03 | 11 | 114.62 | 8 | 167.65 | GPFRA | Female |
| 2018 | 10 | Polina Edmunds | US | 56.31 | 9 | 101.46 | 10 | 157.77 | GPFRA | Female |
| 2018 | 11 | Laurine Lecavelier | FR | 60.68 | 7 | 93.67 | 11 | 154.35 | GPFRA | Female |
| 2018 | 1 | Javier Fernández | ES | 107.86 | 1 | 175.85 | 2 | 283.71 | GPFRA | Male |
| 2018 | 2 | Shoma Uno | JP | 93.92 | 2 | 179.40 | 1 | 273.32 | GPFRA | Male |
| 2018 | 3 | Misha Ge | UZ | 85.41 | 6 | 172.93 | 3 | 258.34 | GPFRA | Male |
| 2018 | 4 | Alexander Samarin | RU | 91.51 | 3 | 161.62 | 4 | 253.13 | GPFRA | Male |
| 2018 | 5 | Alexei Bychenko | IL | 86.79 | 5 | 160.65 | 5 | 247.44 | GPFRA | Male |
| 2018 | 6 | Morisi Kvitelashvili | GE | 86.98 | 4 | 153.52 | 8 | 240.50 | GPFRA | Male |
| 2018 | 7 | Max Aaron | US | 78.64 | 8 | 158.56 | 6 | 237.20 | GPFRA | Male |
| 2018 | 8 | Denis Ten | KZ | 83.70 | 7 | 144.87 | 10 | 228.57 | GPFRA | Male |
| 2018 | 9 | Vincent Zhou | US | 66.12 | 10 | 156.09 | 7 | 222.21 | GPFRA | Male |
| 2018 | 10 | Kévin Aymoz | FR | 70.00 | 9 | 150.43 | 9 | 220.43 | GPFRA | Male |
| 2018 | 11 | Romain Ponsart | FR | 63.81 | 11 | 134.31 | 11 | 198.12 | GPFRA | Male |
| 2018 | 1 | Evgenia Medvedeva | RU | 79.99 | 1 | 144.40 | 1 | 224.39 | GPJPN | Female |
| 2018 | 2 | Carolina Kostner | IT | 74.57 | 2 | 137.67 | 3 | 212.24 | GPJPN | Female |
| 2018 | 3 | Polina Tsurskaya | RU | 70.04 | 3 | 140.15 | 2 | 210.19 | GPJPN | Female |
| 2018 | 4 | Mirai Nagasu | US | 65.17 | 5 | 129.29 | 4 | 194.46 | GPJPN | Female |
| 2018 | 5 | Satoko Miyahara | JP | 65.05 | 6 | 126.75 | 6 | 191.80 | GPJPN | Female |
| 2018 | 6 | Alena Leonova | RU | 63.61 | 7 | 127.34 | 5 | 190.95 | GPJPN | Female |
| 2018 | 7 | Rika Hongo | JP | 65.83 | 4 | 122.00 | 7 | 187.83 | GPJPN | Female |
| 2018 | 8 | Yuna Shiraiwa | JP | 57.34 | 8 | 114.60 | 8 | 171.94 | GPJPN | Female |
| 2018 | 9 | Mariah Bell | US | 57.25 | 9 | 108.79 | 10 | 166.04 | GPJPN | Female |
| 2018 | 10 | Nicole Rajičová | SK | 53.36 | 10 | 106.42 | 11 | 159.78 | GPJPN | Female |
| 2018 | 11 | Alaine Chartrand | CA | 49.60 | 12 | 109.76 | 9 | 159.36 | GPJPN | Female |
| 2018 | 12 | Soyoun Park | KR | 51.54 | 11 | 84.25 | 12 | 135.79 | GPJPN | Female |
| 2018 | 1 | Sergei Voronov | RU | 90.06 | 1 | 181.06 | 1 | 271.12 | GPJPN | Male |
| 2018 | 2 | Adam Rippon | US | 84.95 | 4 | 177.04 | 2 | 261.99 | GPJPN | Male |
| 2018 | 3 | Alexei Bychenko | IL | 85.52 | 2 | 166.55 | 3 | 252.07 | GPJPN | Male |
| 2018 | 4 | Jason Brown | US | 85.36 | 3 | 160.59 | 4 | 245.95 | GPJPN | Male |
| 2018 | 5 | Keegan Messing | CA | 80.13 | 5 | 155.67 | 6 | 235.80 | GPJPN | Male |
| 2018 | 6 | Deniss Vasiljevs | LV | 76.51 | 8 | 158.29 | 5 | 234.80 | GPJPN | Male |
| 2018 | 7 | Kazuki Tomono | JP | 79.88 | 6 | 152.05 | 7 | 231.93 | GPJPN | Male |
| 2018 | 8 | Dmitri Aliev | RU | 77.51 | 7 | 145.94 | 9 | 223.45 | GPJPN | Male |
| 2018 | 9 | Michal Březina | CZ | 76.24 | 9 | 144.21 | 10 | 220.45 | GPJPN | Male |
| 2018 | 10 | Nam Nguyen | CA | 65.82 | 11 | 148.69 | 8 | 214.51 | GPJPN | Male |
| 2018 | 11 | Hiroaki Sato | JP | 75.95 | 10 | 123.25 | 11 | 199.20 | GPJPN | Male |
| 2018 | 1 | Evgenia Medvedeva | RU | 80.75 | 1 | 150.46 | 1 | 231.21 | GPRUS | Female |
| 2018 | 2 | Carolina Kostner | IT | 74.62 | 2 | 141.36 | 2 | 215.98 | GPRUS | Female |
| 2018 | 3 | Wakaba Higuchi | JP | 69.60 | 3 | 137.57 | 3 | 207.17 | GPRUS | Female |
| 2018 | 4 | Elena Radionova | RU | 68.75 | 5 | 126.77 | 4 | 195.52 | GPRUS | Female |
| 2018 | 5 | Kaori Sakamoto | JP | 68.88 | 4 | 125.12 | 5 | 194.00 | GPRUS | Female |
| 2018 | 6 | Mariah Bell | US | 63.85 | 7 | 124.71 | 6 | 188.56 | GPRUS | Female |
| 2018 | 7 | Valeria Mikhailova | RU | 63.38 | 8 | 121.71 | 8 | 185.09 | GPRUS | Female |
| 2018 | 8 | Elizabet Tursynbayeva | KZ | 63.92 | 6 | 121.03 | 9 | 184.95 | GPRUS | Female |
| 2018 | 9 | Mirai Nagasu | US | 56.15 | 9 | 122.10 | 7 | 178.25 | GPRUS | Female |
| 2018 | 10 | Nicole Schott | DE | 55.55 | 10 | 113.17 | 10 | 168.72 | GPRUS | Female |
| 2018 | 11 | Maé-Bérénice Méité | FR | 54.24 | 11 | 106.72 | 12 | 160.96 | GPRUS | Female |
| 2018 | 12 | Anastasiya Galustyan | AM | 48.10 | 12 | 106.80 | 11 | 154.90 | GPRUS | Female |
| 2018 | 1 | Nathan Chen | US | 100.54 | 1 | 193.25 | 2 | 293.79 | GPRUS | Male |
| 2018 | 2 | Yuzuru Hanyu | JP | 94.85 | 2 | 195.92 | 1 | 290.77 | GPRUS | Male |
| 2018 | 3 | Mikhail Kolyada | RU | 85.79 | 4 | 185.27 | 3 | 271.06 | GPRUS | Male |
| 2018 | 4 | Misha Ge | UZ | 85.02 | 5 | 170.31 | 4 | 255.33 | GPRUS | Male |
| 2018 | 5 | Morisi Kvitelashvili | GE | 80.67 | 8 | 169.59 | 5 | 250.26 | GPRUS | Male |
| 2018 | 6 | Dmitri Aliev | RU | 88.77 | 3 | 150.84 | 7 | 239.61 | GPRUS | Male |
| 2018 | 7 | Nam Nguyen | CA | 80.74 | 7 | 157.71 | 6 | 238.45 | GPRUS | Male |
| 2018 | 8 | Deniss Vasiljevs | LV | 82.44 | 6 | 145.09 | 9 | 227.53 | GPRUS | Male |
| 2018 | 9 | Denis Ten | KZ | 69.00 | 10 | 145.35 | 8 | 214.35 | GPRUS | Male |
| 2018 | 10 | Andrei Lazukin | RU | 78.54 | 9 | 133.60 | 11 | 212.14 | GPRUS | Male |
| 2018 | 11 | Grant Hochstein | US | 67.56 | 11 | 138.53 | 10 | 206.09 | GPRUS | Male |
| 2018 | 12 | Daniel Samohin | IL | 62.02 | 12 | 121.77 | 12 | 183.79 | GPRUS | Male |
| 2018 | 1 | Satoko Miyahara | JP | 70.72 | 1 | 143.31 | 1 | 214.03 | GPUSA | Female |
| 2018 | 2 | Kaori Sakamoto | JP | 69.40 | 2 | 141.19 | 2 | 210.59 | GPUSA | Female |
| 2018 | 3 | Bradie Tennell | US | 67.01 | 4 | 137.09 | 3 | 204.10 | GPUSA | Female |
| 2018 | 4 | Polina Tsurskaya | RU | 63.20 | 8 | 132.36 | 4 | 195.56 | GPUSA | Female |
| 2018 | 5 | Serafima Sakhanovich | RU | 66.28 | 5 | 123.47 | 5 | 189.75 | GPUSA | Female |
| 2018 | 6 | Gabrielle Daleman | CA | 68.08 | 3 | 121.06 | 8 | 189.14 | GPUSA | Female |
| 2018 | 7 | Alena Leonova | RU | 63.91 | 7 | 122.02 | 7 | 185.93 | GPUSA | Female |
| 2018 | 8 | Karen Chen | US | 59.53 | 9 | 123.27 | 6 | 182.80 | GPUSA | Female |
| 2018 | 9 | Nicole Rajičová | SK | 55.43 | 10 | 112.18 | 9 | 167.61 | GPUSA | Female |
| 2018 | 10 | Xiangning Li | CN | 55.24 | 11 | 109.08 | 10 | 164.32 | GPUSA | Female |
| 2018 | NA | Ashley Wagner | US | 64.12 | 6 | NA | NA | NA | GPUSA | Female |
| 2018 | 1 | Nathan Chen | US | 104.12 | 1 | 171.76 | 2 | 275.88 | GPUSA | Male |
| 2018 | 2 | Adam Rippon | US | 89.04 | 2 | 177.41 | 1 | 266.45 | GPUSA | Male |
| 2018 | 3 | Sergei Voronov | RU | 87.51 | 3 | 169.98 | 3 | 257.49 | GPUSA | Male |
| 2018 | 4 | Boyang Jin | CN | 77.97 | 6 | 168.06 | 4 | 246.03 | GPUSA | Male |
| 2018 | 5 | Han Yan | CN | 85.97 | 4 | 142.36 | 7 | 228.33 | GPUSA | Male |
| 2018 | 6 | Ross Miner | US | 71.59 | 8 | 148.03 | 5 | 219.62 | GPUSA | Male |
| 2018 | 7 | Takahito Mura | JP | 75.05 | 7 | 137.72 | 8 | 212.77 | GPUSA | Male |
| 2018 | 8 | Liam Firus | CA | 65.17 | 11 | 145.66 | 6 | 210.83 | GPUSA | Male |
| 2018 | 9 | Kevin Reynolds | CA | 69.10 | 10 | 134.95 | 9 | 204.05 | GPUSA | Male |
| 2018 | 10 | Roman Sadovsky | CA | 70.85 | 9 | 129.25 | 10 | 200.10 | GPUSA | Male |
| 2018 | NA | Daniel Samohin | IL | 82.28 | 5 | NA | NA | NA | GPUSA | Male |
| 2018 | NA | Maxim Kovtun | RU | 64.98 | 12 | NA | NA | NA | GPUSA | Male |
| 2018 | 1 | Alina Zagitova | RU | 82.92 | 1 | 156.65 | 2 | 239.57 | OLY | Female |
| 2018 | 2 | Evgenia Medvedeva | RU | 81.61 | 2 | 156.65 | 1 | 238.26 | OLY | Female |
| 2018 | 3 | Kaetlyn Osmond | CA | 78.87 | 3 | 152.15 | 3 | 231.02 | OLY | Female |
| 2018 | 4 | Satoko Miyahara | JP | 75.94 | 4 | 146.44 | 4 | 222.38 | OLY | Female |
| 2018 | 5 | Carolina Kostner | IT | 73.15 | 6 | 139.29 | 5 | 212.44 | OLY | Female |
| 2018 | 6 | Kaori Sakamoto | JP | 73.18 | 5 | 136.53 | 6 | 209.71 | OLY | Female |
| 2018 | 7 | Dabin Choi | KR | 67.77 | 8 | 131.49 | 8 | 199.26 | OLY | Female |
| 2018 | 8 | Maria Sotskova | RU | 63.86 | 12 | 134.24 | 7 | 198.10 | OLY | Female |
| 2018 | 9 | Bradie Tennell | US | 64.01 | 11 | 128.34 | 9 | 192.35 | OLY | Female |
| 2018 | 10 | Mirai Nagasu | US | 66.93 | 9 | 119.61 | 12 | 186.54 | OLY | Female |
| 2018 | 11 | Karen Chen | US | 65.90 | 10 | 119.75 | 11 | 185.65 | OLY | Female |
| 2018 | 12 | Elizabet Tursynbayeva | KZ | 58.82 | 15 | 118.30 | 13 | 177.12 | OLY | Female |
| 2018 | 13 | Hanul Kim | KR | 54.33 | 21 | 121.38 | 10 | 175.71 | OLY | Female |
| 2018 | 14 | Nicole Rajičová | SK | 60.59 | 13 | 114.60 | 15 | 175.19 | OLY | Female |
| 2018 | 15 | Gabrielle Daleman | CA | 68.90 | 7 | 103.56 | 19 | 172.46 | OLY | Female |
| 2018 | 16 | Loena Hendrickx | BE | 55.16 | 20 | 116.72 | 14 | 171.88 | OLY | Female |
| 2018 | 17 | Kailani Craine | AU | 56.77 | 16 | 111.84 | 16 | 168.61 | OLY | Female |
| 2018 | 18 | Nicole Schott | DE | 59.20 | 14 | 109.26 | 17 | 168.46 | OLY | Female |
| 2018 | 19 | Maé-Bérénice Méité | FR | 53.67 | 22 | 106.25 | 18 | 159.92 | OLY | Female |
| 2018 | 20 | Emmi Peltonen | FI | 55.28 | 18 | 101.86 | 21 | 157.14 | OLY | Female |
| 2018 | 21 | Alexia Paganini | CH | 55.26 | 19 | 101.00 | 22 | 156.26 | OLY | Female |
| 2018 | 22 | Xiangning Li | CN | 52.46 | 24 | 101.97 | 20 | 154.43 | OLY | Female |
| 2018 | 23 | Ivett Tóth | HU | 53.22 | 23 | 97.21 | 23 | 150.43 | OLY | Female |
| 2018 | 24 | Isadora Williams | BR | 55.74 | 17 | 88.44 | 24 | 144.18 | OLY | Female |
| 2018 | 25 | Larkyn Austman | CA | 51.42 | 25 | NA | NA | NA | OLY | Female |
| 2018 | 26 | Diana Nikitina | LV | 51.12 | 26 | NA | NA | NA | OLY | Female |
| 2018 | 27 | Giada Russo | IT | 50.88 | 27 | NA | NA | NA | OLY | Female |
| 2018 | 28 | Anita Östlund | SE | 49.14 | 28 | NA | NA | NA | OLY | Female |
| 2018 | 29 | Anna Khnychenkova | UA | 47.59 | 29 | NA | NA | NA | OLY | Female |
| 2018 | 30 | Aiza Imambek | KZ | 44.40 | 30 | NA | NA | NA | OLY | Female |
| 2018 | 1 | Yuzuru Hanyu | JP | 111.68 | 1 | 206.17 | 2 | 317.85 | OLY | Male |
| 2018 | 2 | Shoma Uno | JP | 104.17 | 3 | 202.73 | 3 | 306.90 | OLY | Male |
| 2018 | 3 | Javier Fernández | ES | 107.58 | 2 | 197.66 | 4 | 305.24 | OLY | Male |
| 2018 | 4 | Boyang Jin | CN | 103.32 | 4 | 194.45 | 5 | 297.77 | OLY | Male |
| 2018 | 5 | Nathan Chen | US | 82.27 | 17 | 215.08 | 1 | 297.35 | OLY | Male |
| 2018 | 6 | Vincent Zhou | US | 84.53 | 12 | 192.16 | 6 | 276.69 | OLY | Male |
| 2018 | 7 | Dmitri Aliev | RU | 98.98 | 5 | 168.53 | 13 | 267.51 | OLY | Male |
| 2018 | 8 | Mikhail Kolyada | RU | 86.69 | 8 | 177.56 | 7 | 264.25 | OLY | Male |
| 2018 | 9 | Patrick Chan | CA | 90.01 | 6 | 173.42 | 8 | 263.43 | OLY | Male |
| 2018 | 10 | Adam Rippon | US | 87.95 | 7 | 171.41 | 10 | 259.36 | OLY | Male |
| 2018 | 11 | Alexei Bychenko | IL | 84.13 | 13 | 172.88 | 9 | 257.01 | OLY | Male |
| 2018 | 12 | Keegan Messing | CA | 85.11 | 10 | 170.32 | 12 | 255.43 | OLY | Male |
| 2018 | 13 | Daniel Samohin | IL | 80.69 | 18 | 170.75 | 11 | 251.44 | OLY | Male |
| 2018 | 14 | Jorik Hendrickx | BE | 84.74 | 11 | 164.21 | 16 | 248.95 | OLY | Male |
| 2018 | 15 | Junhwan Cha | KR | 83.43 | 15 | 165.16 | 14 | 248.59 | OLY | Male |
| 2018 | 16 | Michal Březina | CZ | 85.15 | 9 | 160.92 | 18 | 246.07 | OLY | Male |
| 2018 | 17 | Misha Ge | UZ | 83.90 | 14 | 161.04 | 17 | 244.94 | OLY | Male |
| 2018 | 18 | Keiji Tanaka | JP | 80.05 | 20 | 164.78 | 15 | 244.83 | OLY | Male |
| 2018 | 19 | Deniss Vasiljevs | LV | 79.52 | 21 | 155.06 | 20 | 234.58 | OLY | Male |
| 2018 | 20 | Brendan Kerry | AU | 83.06 | 16 | 150.75 | 21 | 233.81 | OLY | Male |
| 2018 | 21 | Matteo Rizzo | IT | 75.63 | 23 | 156.78 | 19 | 232.41 | OLY | Male |
| 2018 | 22 | Paul Fentz | DE | 74.73 | 24 | 139.82 | 22 | 214.55 | OLY | Male |
| 2018 | 23 | Han Yan | CN | 80.63 | 19 | 132.38 | 23 | 213.01 | OLY | Male |
| 2018 | 24 | Morisi Kvitelashvili | GE | 76.56 | 22 | 128.01 | 24 | 204.57 | OLY | Male |
| 2018 | 25 | Julian Zhi-Jie Yee | MY | 73.58 | 25 | NA | NA | NA | OLY | Male |
| 2018 | 26 | Chafik Besseghier | FR | 72.10 | 26 | NA | NA | NA | OLY | Male |
| 2018 | 27 | Denis Ten | KZ | 70.12 | 27 | NA | NA | NA | OLY | Male |
| 2018 | 28 | Michael Christian Martinez | PH | 55.56 | 28 | NA | NA | NA | OLY | Male |
| 2018 | 29 | Felipe Montoya Pulgarín | ES | 52.41 | 29 | NA | NA | NA | OLY | Male |
| 2018 | 30 | Yaroslav Paniot | UA | 46.58 | 30 | NA | NA | NA | OLY | Male |
| 2017 | 1 | Mai Mihara | JP | 66.51 | 4 | 134.34 | 1 | 200.85 | 4CC | Female |
| 2017 | 2 | Gabrielle Daleman | CA | 68.25 | 1 | 128.66 | 3 | 196.91 | 4CC | Female |
| 2017 | 3 | Mirai Nagasu | US | 62.91 | 5 | 132.04 | 2 | 194.95 | 4CC | Female |
| 2017 | 4 | Kaetlyn Osmond | CA | 68.21 | 2 | 115.96 | 6 | 184.17 | 4CC | Female |
| 2017 | 5 | Dabin Choi | KR | 61.62 | 6 | 120.79 | 4 | 182.41 | 4CC | Female |
| 2017 | 6 | Mariah Bell | US | 61.21 | 7 | 115.89 | 7 | 177.10 | 4CC | Female |
| 2017 | 7 | Zijun Li | CN | 60.37 | 8 | 116.68 | 5 | 177.05 | 4CC | Female |
| 2017 | 8 | Elizabet Tursynbayeva | KZ | 66.87 | 3 | 109.78 | 11 | 176.65 | 4CC | Female |
| 2017 | 9 | Wakaba Higuchi | JP | 58.83 | 10 | 113.22 | 9 | 172.05 | 4CC | Female |
| 2017 | 10 | Rika Hongo | JP | 59.16 | 9 | 108.26 | 13 | 167.42 | 4CC | Female |
| 2017 | 11 | Alaine Chartrand | CA | 53.64 | 14 | 113.48 | 8 | 167.12 | 4CC | Female |
| 2017 | 12 | Karen Chen | US | 55.60 | 12 | 111.22 | 10 | 166.82 | 4CC | Female |
| 2017 | 13 | Xiangning Li | CN | 55.73 | 11 | 109.12 | 12 | 164.85 | 4CC | Female |
| 2017 | 14 | Brooklee Han | AU | 48.44 | 16 | 103.61 | 14 | 152.05 | 4CC | Female |
| 2017 | 15 | Ziquan Zhao | CN | 49.97 | 15 | 97.40 | 15 | 147.37 | 4CC | Female |
| 2017 | 16 | Kailani Craine | AU | 54.70 | 13 | 82.21 | 17 | 136.91 | 4CC | Female |
| 2017 | 17 | Amy Lin | TW | 45.40 | 18 | 79.62 | 19 | 125.02 | 4CC | Female |
| 2017 | 18 | Maisy Hiu Ching Ma | HK | 43.55 | 19 | 81.10 | 18 | 124.65 | 4CC | Female |
| 2017 | 19 | Suhhyun Son | KR | 38.61 | 22 | 83.74 | 16 | 122.35 | 4CC | Female |
| 2017 | 20 | Chloe Ing | SG | 41.61 | 21 | 78.57 | 20 | 120.18 | 4CC | Female |
| 2017 | 21 | Shuran Yu | SG | 43.26 | 20 | 75.14 | 21 | 118.40 | 4CC | Female |
| 2017 | NA | Nahyun Kim | KR | 45.95 | 17 | NA | NA | NA | 4CC | Female |
| 2017 | NA | Michaela Du Toit | ZA | 33.39 | 23 | NA | NA | NA | 4CC | Female |
| 2017 | 1 | Nathan Chen | US | 103.12 | 1 | 204.34 | 2 | 307.46 | 4CC | Male |
| 2017 | 2 | Yuzuru Hanyu | JP | 97.04 | 3 | 206.67 | 1 | 303.71 | 4CC | Male |
| 2017 | 3 | Shoma Uno | JP | 100.28 | 2 | 187.77 | 3 | 288.05 | 4CC | Male |
| 2017 | 4 | Patrick Chan | CA | 88.46 | 5 | 179.52 | 4 | 267.98 | 4CC | Male |
| 2017 | 5 | Boyang Jin | CN | 91.33 | 4 | 176.18 | 5 | 267.51 | 4CC | Male |
| 2017 | 6 | Jason Brown | US | 80.77 | 9 | 165.08 | 6 | 245.85 | 4CC | Male |
| 2017 | 7 | Misha Ge | UZ | 81.85 | 8 | 157.56 | 8 | 239.41 | 4CC | Male |
| 2017 | 8 | Nam Nguyen | CA | 72.99 | 13 | 164.09 | 7 | 237.08 | 4CC | Male |
| 2017 | 9 | Grant Hochstein | US | 81.94 | 7 | 153.78 | 9 | 235.72 | 4CC | Male |
| 2017 | 10 | Han Yan | CN | 84.08 | 6 | 151.37 | 10 | 235.45 | 4CC | Male |
| 2017 | 11 | Brendan Kerry | AU | 78.11 | 10 | 149.28 | 11 | 227.39 | 4CC | Male |
| 2017 | 12 | Kevin Reynolds | CA | 76.36 | 12 | 145.95 | 12 | 222.31 | 4CC | Male |
| 2017 | 13 | Keiji Tanaka | JP | 77.55 | 11 | 142.63 | 13 | 220.18 | 4CC | Male |
| 2017 | 14 | Michael Christian Martinez | PH | 72.47 | 14 | 141.68 | 14 | 214.15 | 4CC | Male |
| 2017 | 15 | Julian Zhi-Jie Yee | MY | 72.21 | 15 | 130.46 | 16 | 202.67 | 4CC | Male |
| 2017 | 16 | Sihyeong Lee | KR | 65.40 | 17 | 130.32 | 17 | 195.72 | 4CC | Male |
| 2017 | 17 | Jinseo Kim | KR | 64.26 | 18 | 130.79 | 15 | 195.05 | 4CC | Male |
| 2017 | 18 | Junehyoung Lee | KR | 67.55 | 16 | 120.03 | 18 | 187.58 | 4CC | Male |
| 2017 | 19 | Chih-I Tsao | TW | 51.02 | 22 | 118.61 | 19 | 169.63 | 4CC | Male |
| 2017 | 20 | Andrew Dodds | AU | 60.17 | 19 | 101.88 | 21 | 162.05 | 4CC | Male |
| 2017 | 21 | Mark Webster | AU | 54.92 | 20 | 105.11 | 20 | 160.03 | 4CC | Male |
| 2017 | 22 | Leslie Man Cheuk Ip | HK | 52.86 | 21 | 93.88 | 22 | 146.74 | 4CC | Male |
| 2017 | 23 | Kai Xiang Chew | MY | 47.38 | 23 | 91.08 | 23 | 138.46 | 4CC | Male |
| 2017 | 24 | Micah Tang | TW | 46.41 | 24 | 89.38 | 24 | 135.79 | 4CC | Male |
| 2017 | 25 | Harry Hau Yin Lee | HK | 45.27 | 25 | NA | NA | NA | 4CC | Male |
| 2017 | 26 | Harrison Jon Yen Wong | HK | 45.12 | 26 | NA | NA | NA | 4CC | Male |
| 2017 | 1 | Evgenia Medvedeva | RU | 78.92 | 1 | 150.79 | 1 | 229.71 | EC | Female |
| 2017 | 2 | Anna Pogorilaya | RU | 74.39 | 2 | 137.00 | 3 | 211.39 | EC | Female |
| 2017 | 3 | Carolina Kostner | IT | 72.40 | 3 | 138.12 | 2 | 210.52 | EC | Female |
| 2017 | 4 | Maria Sotskova | RU | 72.17 | 4 | 120.35 | 5 | 192.52 | EC | Female |
| 2017 | 5 | Laurine Lecavelier | FR | 63.81 | 5 | 124.29 | 4 | 188.10 | EC | Female |
| 2017 | 6 | Nicole Rajičová | SK | 60.98 | 7 | 118.72 | 6 | 179.70 | EC | Female |
| 2017 | 7 | Loena Hendrickx | BE | 55.41 | 11 | 117.30 | 7 | 172.71 | EC | Female |
| 2017 | 8 | Ivett Tóth | HU | 61.49 | 6 | 111.16 | 8 | 172.65 | EC | Female |
| 2017 | 9 | Roberta Rodeghiero | IT | 57.77 | 8 | 103.23 | 12 | 161.00 | EC | Female |
| 2017 | 10 | Nicole Schott | DE | 56.88 | 9 | 103.75 | 10 | 160.63 | EC | Female |
| 2017 | 11 | Emmi Peltonen | FI | 53.52 | 14 | 107.05 | 9 | 160.57 | EC | Female |
| 2017 | 12 | Anastasiya Galustyan | AM | 56.40 | 10 | 98.74 | 14 | 155.14 | EC | Female |
| 2017 | 13 | Matilda Algotsson | SE | 51.35 | 18 | 103.28 | 11 | 154.63 | EC | Female |
| 2017 | 14 | Joshi Helgesson | SE | 53.93 | 13 | 98.93 | 13 | 152.86 | EC | Female |
| 2017 | 15 | Helery Hälvin | EE | 51.72 | 16 | 94.96 | 15 | 146.68 | EC | Female |
| 2017 | 16 | Maé-Bérénice Méité | FR | 54.96 | 12 | 90.11 | 19 | 145.07 | EC | Female |
| 2017 | 17 | Nathalie Weinzierl | DE | 48.70 | 22 | 94.70 | 17 | 143.40 | EC | Female |
| 2017 | 18 | Natasha Mckay | GB | 45.97 | 24 | 94.88 | 16 | 140.85 | EC | Female |
| 2017 | 19 | Angelina Kuchvalska | LV | 49.05 | 20 | 90.58 | 18 | 139.63 | EC | Female |
| 2017 | 20 | Michaela Hanzlikova | CZ | 52.39 | 15 | 85.84 | 21 | 138.23 | EC | Female |
| 2017 | 21 | Anna Khnychenkova | UA | 48.93 | 21 | 87.64 | 20 | 136.57 | EC | Female |
| 2017 | 22 | Kerstin Frank | AT | 51.47 | 17 | 80.61 | 24 | 132.08 | EC | Female |
| 2017 | 23 | Viveca Lindfors | FI | 49.48 | 19 | 80.62 | 22 | 130.10 | EC | Female |
| 2017 | 24 | Anne Line Gjersem | NO | 48.06 | 23 | 80.62 | 23 | 128.68 | EC | Female |
| 2017 | 25 | Julia Sauter | RO | 45.59 | 25 | NA | NA | NA | EC | Female |
| 2017 | 26 | Daša Grm | SI | 43.48 | 26 | NA | NA | NA | EC | Female |
| 2017 | 27 | Yasmine Kimiko Yamada | CH | 42.33 | 27 | NA | NA | NA | EC | Female |
| 2017 | 28 | Elzbieta Kropa | LT | 41.52 | 28 | NA | NA | NA | EC | Female |
| 2017 | 29 | Antonina Dubinina | RS | 41.05 | 29 | NA | NA | NA | EC | Female |
| 2017 | 30 | Colette Kaminski | PL | 39.83 | 30 | NA | NA | NA | EC | Female |
| 2017 | 31 | Aimee Buchanan | IL | 38.49 | 31 | NA | NA | NA | EC | Female |
| 2017 | 32 | Birce Atabey | TR | 35.59 | 32 | NA | NA | NA | EC | Female |
| 2017 | 33 | Valentina Matos | ES | 34.79 | 33 | NA | NA | NA | EC | Female |
| 2017 | 34 | Hristina Vassileva | BG | 24.55 | 34 | NA | NA | NA | EC | Female |
| 2017 | 1 | Javier Fernández | ES | 104.25 | 1 | 190.59 | 1 | 294.84 | EC | Male |
| 2017 | 2 | Maxim Kovtun | RU | 94.53 | 2 | 172.27 | 2 | 266.80 | EC | Male |
| 2017 | 3 | Mikhail Kolyada | RU | 83.96 | 4 | 166.22 | 3 | 250.18 | EC | Male |
| 2017 | 4 | Jorik Hendrickx | BE | 82.50 | 5 | 160.06 | 5 | 242.56 | EC | Male |
| 2017 | 5 | Alexei Bychenko | IL | 86.68 | 3 | 152.56 | 9 | 239.24 | EC | Male |
| 2017 | 6 | Morisi Kvitelashvili | GE | 76.85 | 10 | 161.35 | 4 | 238.20 | EC | Male |
| 2017 | 7 | Deniss Vasiljevs | LV | 79.87 | 6 | 155.33 | 6 | 235.20 | EC | Male |
| 2017 | 8 | Alexander Samarin | RU | 77.26 | 9 | 153.61 | 7 | 230.87 | EC | Male |
| 2017 | 9 | Chafik Besseghier | FR | 76.19 | 11 | 151.40 | 10 | 227.59 | EC | Male |
| 2017 | 10 | Paul Fentz | DE | 72.68 | 12 | 153.17 | 8 | 225.85 | EC | Male |
| 2017 | 11 | Alexander Majorov | SE | 78.87 | 7 | 139.11 | 12 | 217.98 | EC | Male |
| 2017 | 12 | Michal Březina | CZ | 78.61 | 8 | 136.91 | 13 | 215.52 | EC | Male |
| 2017 | 13 | Ivan Righini | IT | 69.96 | 14 | 140.19 | 11 | 210.15 | EC | Male |
| 2017 | 14 | Ivan Pavlov | UA | 68.94 | 15 | 133.93 | 14 | 202.87 | EC | Male |
| 2017 | 15 | Kévin Aymoz | FR | 71.26 | 13 | 128.21 | 18 | 199.47 | EC | Male |
| 2017 | 16 | Graham Newberry | GB | 67.79 | 16 | 130.27 | 16 | 198.06 | EC | Male |
| 2017 | 17 | Stéphane Walker | CH | 62.86 | 19 | 133.88 | 15 | 196.74 | EC | Male |
| 2017 | 18 | Javier Raya | ES | 66.67 | 17 | 128.87 | 17 | 195.54 | EC | Male |
| 2017 | 19 | Maurizio Zandrón | IT | 63.79 | 18 | 122.61 | 19 | 186.40 | EC | Male |
| 2017 | 20 | Jiri Belohradsky | CZ | 60.99 | 20 | 120.63 | 21 | 181.62 | EC | Male |
| 2017 | 21 | Slavik Hayrapetyan | AM | 60.69 | 21 | 120.09 | 22 | 180.78 | EC | Male |
| 2017 | 22 | Daniel Albert Naurits | EE | 55.14 | 24 | 120.96 | 20 | 176.10 | EC | Male |
| 2017 | 23 | Valtter Virtanen | FI | 56.52 | 22 | 107.57 | 24 | 164.09 | EC | Male |
| 2017 | 24 | Sondre Oddvoll Bøe | NO | 55.24 | 23 | 107.61 | 23 | 162.85 | EC | Male |
| 2017 | 25 | Ihor Reznichenko | PL | 54.81 | 25 | NA | NA | NA | EC | Male |
| 2017 | 26 | Nicholas Vrdoljak | HR | 53.45 | 26 | NA | NA | NA | EC | Male |
| 2017 | 27 | Alexander Borovoj | HU | 53.02 | 27 | NA | NA | NA | EC | Male |
| 2017 | 28 | Thomas Kennes | NL | 52.95 | 28 | NA | NA | NA | EC | Male |
| 2017 | 29 | Anton Karpuk | BY | 52.26 | 29 | NA | NA | NA | EC | Male |
| 2017 | 30 | Mark Gorodnitsky | IL | 51.72 | 30 | NA | NA | NA | EC | Male |
| 2017 | 31 | Larry Loupolover | AZ | 51.30 | 31 | NA | NA | NA | EC | Male |
| 2017 | 32 | Engin Ali Artan | TR | 50.38 | 32 | NA | NA | NA | EC | Male |
| 2017 | 33 | Daniel Samohin | IL | 50.33 | 33 | NA | NA | NA | EC | Male |
| 2017 | 34 | Michael Neuman | SK | 47.67 | 34 | NA | NA | NA | EC | Male |
| 2017 | 35 | Nicky-Leo Obreykov | BG | 44.83 | 35 | NA | NA | NA | EC | Male |
| 2017 | 36 | Mario-Rafaël Ionian | AT | 42.62 | 36 | NA | NA | NA | EC | Male |
| 2017 | 1 | Evgenia Medvedeva | RU | 76.24 | 1 | 144.41 | 1 | 220.65 | GPCAN | Female |
| 2017 | 2 | Kaetlyn Osmond | CA | 74.33 | 2 | 132.12 | 2 | 206.45 | GPCAN | Female |
| 2017 | 3 | Satoko Miyahara | JP | 65.24 | 5 | 126.84 | 3 | 192.08 | GPCAN | Female |
| 2017 | 4 | Elizaveta Tuktamysheva | RU | 66.79 | 3 | 121.20 | 5 | 187.99 | GPCAN | Female |
| 2017 | 5 | Alaine Chartrand | CA | 62.15 | 6 | 123.41 | 4 | 185.56 | GPCAN | Female |
| 2017 | 6 | Rika Hongo | JP | 65.75 | 4 | 105.44 | 8 | 171.19 | GPCAN | Female |
| 2017 | 7 | Dabin Choi | KR | 53.29 | 8 | 112.49 | 6 | 165.78 | GPCAN | Female |
| 2017 | 8 | Nahyun Kim | KR | 60.46 | 7 | 104.02 | 9 | 164.48 | GPCAN | Female |
| 2017 | 9 | Mirai Nagasu | US | 53.19 | 9 | 98.23 | 11 | 151.42 | GPCAN | Female |
| 2017 | 10 | Joshi Helgesson | SE | 49.72 | 10 | 100.05 | 10 | 149.77 | GPCAN | Female |
| 2017 | 11 | Yuka Nagai | JP | 40.39 | 11 | 107.17 | 7 | 147.56 | GPCAN | Female |
| 2017 | 1 | Patrick Chan | CA | 90.56 | 1 | 176.39 | 2 | 266.95 | GPCAN | Male |
| 2017 | 2 | Yuzuru Hanyu | JP | 79.65 | 4 | 183.41 | 1 | 263.06 | GPCAN | Male |
| 2017 | 3 | Kevin Reynolds | CA | 80.57 | 3 | 164.49 | 3 | 245.06 | GPCAN | Male |
| 2017 | 4 | Michal Březina | CZ | 70.36 | 9 | 157.06 | 4 | 227.42 | GPCAN | Male |
| 2017 | 5 | Daniel Samohin | IL | 74.62 | 5 | 151.91 | 7 | 226.53 | GPCAN | Male |
| 2017 | 6 | Misha Ge | UZ | 72.30 | 7 | 153.77 | 5 | 226.07 | GPCAN | Male |
| 2017 | 7 | Alexander Petrov | RU | 71.50 | 8 | 152.89 | 6 | 224.39 | GPCAN | Male |
| 2017 | 8 | Takahito Mura | JP | 81.24 | 2 | 140.89 | 9 | 222.13 | GPCAN | Male |
| 2017 | 9 | Liam Firus | CA | 70.09 | 10 | 140.80 | 10 | 210.89 | GPCAN | Male |
| 2017 | 10 | Han Yan | CN | 72.86 | 6 | 136.25 | 11 | 209.11 | GPCAN | Male |
| 2017 | 11 | Grant Hochstein | US | 60.20 | 12 | 144.49 | 8 | 204.69 | GPCAN | Male |
| 2017 | 12 | Ross Miner | US | 63.92 | 11 | 132.61 | 12 | 196.53 | GPCAN | Male |
| 2017 | 1 | Elena Radionova | RU | 70.75 | 2 | 135.15 | 1 | 205.90 | GPCHN | Female |
| 2017 | 2 | Kaetlyn Osmond | CA | 72.20 | 1 | 123.80 | 3 | 196.00 | GPCHN | Female |
| 2017 | 3 | Elizaveta Tuktamysheva | RU | 64.88 | 4 | 127.69 | 2 | 192.57 | GPCHN | Female |
| 2017 | 4 | Mai Mihara | JP | 68.48 | 3 | 122.44 | 4 | 190.92 | GPCHN | Female |
| 2017 | 5 | Rika Hongo | JP | 63.63 | 6 | 118.12 | 6 | 181.75 | GPCHN | Female |
| 2017 | 6 | Ashley Wagner | US | 64.36 | 5 | 117.02 | 7 | 181.38 | GPCHN | Female |
| 2017 | 7 | Karen Chen | US | 58.28 | 9 | 121.11 | 5 | 179.39 | GPCHN | Female |
| 2017 | 8 | Zijun Li | CN | 61.32 | 7 | 111.08 | 8 | 172.40 | GPCHN | Female |
| 2017 | 9 | Courtney Hicks | US | 59.86 | 8 | 103.78 | 9 | 163.64 | GPCHN | Female |
| 2017 | 10 | Xiangning Li | CN | 54.55 | 11 | 102.72 | 10 | 157.27 | GPCHN | Female |
| 2017 | 11 | Ziquan Zhao | CN | 58.20 | 10 | 90.92 | 12 | 149.12 | GPCHN | Female |
| 2017 | 12 | Joshi Helgesson | SE | 49.25 | 12 | 95.39 | 11 | 144.64 | GPCHN | Female |
| 2017 | 1 | Patrick Chan | CA | 83.41 | 3 | 196.31 | 1 | 279.72 | GPCHN | Male |
| 2017 | 2 | Boyang Jin | CN | 96.17 | 1 | 182.37 | 2 | 278.54 | GPCHN | Male |
| 2017 | 3 | Sergei Voronov | RU | 82.93 | 4 | 160.83 | 4 | 243.76 | GPCHN | Male |
| 2017 | 4 | Max Aaron | US | 81.67 | 5 | 161.07 | 3 | 242.74 | GPCHN | Male |
| 2017 | 5 | Han Yan | CN | 75.04 | 8 | 155.15 | 5 | 230.19 | GPCHN | Male |
| 2017 | 6 | Alexander Petrov | RU | 74.21 | 9 | 154.23 | 6 | 228.44 | GPCHN | Male |
| 2017 | 7 | Maxim Kovtun | RU | 70.10 | 10 | 151.33 | 7 | 221.43 | GPCHN | Male |
| 2017 | 8 | Daniel Samohin | IL | 83.47 | 2 | 130.04 | 10 | 213.51 | GPCHN | Male |
| 2017 | 9 | Ross Miner | US | 76.73 | 6 | 136.61 | 8 | 213.34 | GPCHN | Male |
| 2017 | 10 | Michal Březina | CZ | 75.86 | 7 | 135.91 | 9 | 211.77 | GPCHN | Male |
| 2017 | 1 | Evgenia Medvedeva | RU | 79.21 | 1 | 148.45 | 1 | 227.66 | GPF | Female |
| 2017 | 2 | Satoko Miyahara | JP | 74.64 | 3 | 143.69 | 2 | 218.33 | GPF | Female |
| 2017 | 3 | Anna Pogorilaya | RU | 73.29 | 4 | 143.18 | 3 | 216.47 | GPF | Female |
| 2017 | 4 | Kaetlyn Osmond | CA | 75.54 | 2 | 136.91 | 4 | 212.45 | GPF | Female |
| 2017 | 5 | Maria Sotskova | RU | 65.74 | 6 | 133.05 | 5 | 198.79 | GPF | Female |
| 2017 | 6 | Elena Radionova | RU | 68.98 | 5 | 119.83 | 6 | 188.81 | GPF | Female |
| 2017 | 1 | Yuzuru Hanyu | JP | 106.53 | 1 | 187.37 | 3 | 293.90 | GPF | Male |
| 2017 | 2 | Nathan Chen | US | 85.30 | 5 | 197.55 | 1 | 282.85 | GPF | Male |
| 2017 | 3 | Shoma Uno | JP | 86.82 | 4 | 195.69 | 2 | 282.51 | GPF | Male |
| 2017 | 4 | Javier Fernández | ES | 91.76 | 3 | 177.01 | 4 | 268.77 | GPF | Male |
| 2017 | 5 | Patrick Chan | CA | 99.76 | 2 | 166.99 | 5 | 266.75 | GPF | Male |
| 2017 | 6 | Adam Rippon | US | 83.93 | 6 | 149.17 | 6 | 233.10 | GPF | Male |
| 2017 | 1 | Evgenia Medvedeva | RU | 78.52 | 1 | 143.02 | 1 | 221.54 | GPFRA | Female |
| 2017 | 2 | Maria Sotskova | RU | 68.71 | 3 | 131.64 | 2 | 200.35 | GPFRA | Female |
| 2017 | 3 | Wakaba Higuchi | JP | 65.02 | 5 | 129.46 | 3 | 194.48 | GPFRA | Female |
| 2017 | 4 | Gabrielle Daleman | CA | 72.70 | 2 | 119.40 | 6 | 192.10 | GPFRA | Female |
| 2017 | 5 | Soyoun Park | KR | 64.89 | 6 | 120.30 | 4 | 185.19 | GPFRA | Female |
| 2017 | 6 | Laurine Lecavelier | FR | 66.61 | 4 | 118.04 | 7 | 184.65 | GPFRA | Female |
| 2017 | 7 | Maé-Bérénice Méité | FR | 52.78 | 11 | 119.87 | 5 | 172.65 | GPFRA | Female |
| 2017 | 8 | Gracie Gold | US | 54.87 | 10 | 111.02 | 8 | 165.89 | GPFRA | Female |
| 2017 | 9 | Mao Asada | JP | 61.29 | 8 | 100.10 | 10 | 161.39 | GPFRA | Female |
| 2017 | 10 | Yuka Nagai | JP | 52.41 | 12 | 107.08 | 9 | 159.49 | GPFRA | Female |
| 2017 | 11 | Anastasiya Galustyan | AM | 56.92 | 9 | 98.57 | 11 | 155.49 | GPFRA | Female |
| 2017 | 12 | Alena Leonova | RU | 63.87 | 7 | 77.49 | 12 | 141.36 | GPFRA | Female |
| 2017 | 1 | Javier Fernández | ES | 96.57 | 1 | 188.81 | 1 | 285.38 | GPFRA | Male |
| 2017 | 2 | Denis Ten | KZ | 89.21 | 3 | 180.05 | 3 | 269.26 | GPFRA | Male |
| 2017 | 3 | Adam Rippon | US | 85.25 | 4 | 182.28 | 2 | 267.53 | GPFRA | Male |
| 2017 | 4 | Nathan Chen | US | 92.85 | 2 | 171.95 | 4 | 264.80 | GPFRA | Male |
| 2017 | 5 | Takahito Mura | JP | 78.38 | 6 | 170.04 | 5 | 248.42 | GPFRA | Male |
| 2017 | 6 | Jorik Hendrickx | BE | 80.34 | 5 | 150.13 | 8 | 230.47 | GPFRA | Male |
| 2017 | 7 | Misha Ge | UZ | 72.49 | 8 | 156.57 | 6 | 229.06 | GPFRA | Male |
| 2017 | 8 | Chafik Besseghier | FR | 77.00 | 7 | 148.02 | 9 | 225.02 | GPFRA | Male |
| 2017 | 9 | Artur Dmitriev | RU | 64.48 | 11 | 154.22 | 7 | 218.70 | GPFRA | Male |
| 2017 | 10 | Brendan Kerry | AU | 70.67 | 9 | 128.73 | 10 | 199.40 | GPFRA | Male |
| 2017 | 11 | Ivan Righini | IT | 68.42 | 10 | 117.39 | 11 | 185.81 | GPFRA | Male |
| 2017 | 1 | Anna Pogorilaya | RU | 71.56 | 1 | 139.30 | 1 | 210.86 | GPJPN | Female |
| 2017 | 2 | Satoko Miyahara | JP | 64.20 | 3 | 133.80 | 2 | 198.00 | GPJPN | Female |
| 2017 | 3 | Maria Sotskova | RU | 69.96 | 2 | 125.92 | 3 | 195.88 | GPJPN | Female |
| 2017 | 4 | Wakaba Higuchi | JP | 62.58 | 5 | 122.81 | 4 | 185.39 | GPJPN | Female |
| 2017 | 5 | Mirai Nagasu | US | 63.49 | 4 | 116.84 | 8 | 180.33 | GPJPN | Female |
| 2017 | 6 | Karen Chen | US | 58.76 | 7 | 119.69 | 5 | 178.45 | GPJPN | Female |
| 2017 | 7 | Yura Matsuda | JP | 60.98 | 6 | 117.28 | 7 | 178.26 | GPJPN | Female |
| 2017 | 8 | Elizabet Tursynbayeva | KZ | 55.66 | 9 | 119.45 | 6 | 175.11 | GPJPN | Female |
| 2017 | 9 | Dabin Choi | KR | 51.06 | 11 | 114.57 | 9 | 165.63 | GPJPN | Female |
| 2017 | 10 | Alaine Chartrand | CA | 58.72 | 8 | 101.50 | 11 | 160.22 | GPJPN | Female |
| 2017 | 11 | Nicole Rajičová | SK | 53.43 | 10 | 106.27 | 10 | 159.70 | GPJPN | Female |
| 2017 | 1 | Yuzuru Hanyu | JP | 103.89 | 1 | 197.58 | 1 | 301.47 | GPJPN | Male |
| 2017 | 2 | Nathan Chen | US | 87.94 | 2 | 180.97 | 2 | 268.91 | GPJPN | Male |
| 2017 | 3 | Keiji Tanaka | JP | 80.49 | 3 | 167.95 | 3 | 248.44 | GPJPN | Male |
| 2017 | 4 | Alexei Bychenko | IL | 75.13 | 7 | 154.74 | 4 | 229.87 | GPJPN | Male |
| 2017 | 5 | Mikhail Kolyada | RU | 78.18 | 4 | 147.51 | 6 | 225.69 | GPJPN | Male |
| 2017 | 6 | Deniss Vasiljevs | LV | 70.50 | 10 | 153.23 | 5 | 223.73 | GPJPN | Male |
| 2017 | 7 | Jason Brown | US | 74.33 | 8 | 144.14 | 7 | 218.47 | GPJPN | Male |
| 2017 | 8 | Nam Nguyen | CA | 75.33 | 6 | 137.10 | 8 | 212.43 | GPJPN | Male |
| 2017 | 9 | Ryuju Hino | JP | 72.50 | 9 | 134.65 | 9 | 207.15 | GPJPN | Male |
| 2017 | 10 | Elladj Baldé | CA | 76.29 | 5 | 119.03 | 11 | 195.32 | GPJPN | Male |
| 2017 | 11 | Grant Hochstein | US | 68.31 | 11 | 123.09 | 10 | 191.40 | GPJPN | Male |
| 2017 | 1 | Anna Pogorilaya | RU | 73.93 | 1 | 141.28 | 1 | 215.21 | GPRUS | Female |
| 2017 | 2 | Elena Radionova | RU | 71.93 | 2 | 123.67 | 2 | 195.60 | GPRUS | Female |
| 2017 | 3 | Courtney Hicks | US | 63.68 | 6 | 119.30 | 3 | 182.98 | GPRUS | Female |
| 2017 | 4 | Zijun Li | CN | 63.89 | 5 | 117.94 | 4 | 181.83 | GPRUS | Female |
| 2017 | 5 | Elizabet Tursynbayeva | KZ | 64.31 | 4 | 117.01 | 5 | 181.32 | GPRUS | Female |
| 2017 | 6 | Yura Matsuda | JP | 61.57 | 7 | 116.08 | 6 | 177.65 | GPRUS | Female |
| 2017 | 7 | Nicole Rajičová | SK | 57.91 | 8 | 109.65 | 7 | 167.56 | GPRUS | Female |
| 2017 | 8 | Roberta Rodeghiero | IT | 52.57 | 12 | 107.23 | 8 | 159.80 | GPRUS | Female |
| 2017 | 9 | Anastasiya Galustyan | AM | 55.93 | 9 | 103.33 | 9 | 159.26 | GPRUS | Female |
| 2017 | 10 | Angelina Kuchvalska | LV | 54.29 | 11 | 96.80 | 10 | 151.09 | GPRUS | Female |
| 2017 | 11 | Kanako Murakami | JP | 55.25 | 10 | 95.78 | 11 | 151.03 | GPRUS | Female |
| 2017 | 12 | Julia Lipnitskaia | RU | 69.25 | 3 | 78.88 | 12 | 148.13 | GPRUS | Female |
| 2017 | 1 | Javier Fernández | ES | 91.55 | 2 | 201.43 | 1 | 292.98 | GPRUS | Male |
| 2017 | 2 | Shoma Uno | JP | 98.59 | 1 | 186.48 | 2 | 285.07 | GPRUS | Male |
| 2017 | 3 | Alexei Bychenko | IL | 86.81 | 4 | 168.71 | 3 | 255.52 | GPRUS | Male |
| 2017 | 4 | Mikhail Kolyada | RU | 90.28 | 3 | 155.02 | 6 | 245.30 | GPRUS | Male |
| 2017 | 5 | Max Aaron | US | 73.64 | 8 | 161.94 | 4 | 235.58 | GPRUS | Male |
| 2017 | 6 | Elladj Baldé | CA | 76.36 | 6 | 149.09 | 8 | 225.45 | GPRUS | Male |
| 2017 | 7 | Keiji Tanaka | JP | 69.13 | 10 | 155.78 | 5 | 224.91 | GPRUS | Male |
| 2017 | 8 | Chafik Besseghier | FR | 80.68 | 5 | 143.30 | 10 | 223.98 | GPRUS | Male |
| 2017 | 9 | Gordey Gorshkov | RU | 73.37 | 9 | 150.14 | 7 | 223.51 | GPRUS | Male |
| 2017 | 10 | Artur Dmitriev | RU | 76.06 | 7 | 145.46 | 9 | 221.52 | GPRUS | Male |
| 2017 | 11 | Deniss Vasiljevs | LV | 62.40 | 12 | 141.37 | 11 | 203.77 | GPRUS | Male |
| 2017 | 12 | Alexander Majorov | SE | 67.80 | 11 | 124.34 | 12 | 192.14 | GPRUS | Male |
| 2017 | 1 | Ashley Wagner | US | 69.50 | 1 | 126.94 | 2 | 196.44 | GPUSA | Female |
| 2017 | 2 | Mariah Bell | US | 60.92 | 6 | 130.67 | 1 | 191.59 | GPUSA | Female |
| 2017 | 3 | Mai Mihara | JP | 65.75 | 2 | 123.53 | 3 | 189.28 | GPUSA | Female |
| 2017 | 4 | Gabrielle Daleman | CA | 64.49 | 4 | 122.14 | 4 | 186.63 | GPUSA | Female |
| 2017 | 5 | Gracie Gold | US | 64.87 | 3 | 119.35 | 5 | 184.22 | GPUSA | Female |
| 2017 | 6 | Mao Asada | JP | 64.47 | 5 | 112.31 | 6 | 176.78 | GPUSA | Female |
| 2017 | 7 | Serafima Sakhanovich | RU | 56.52 | 8 | 107.32 | 7 | 163.84 | GPUSA | Female |
| 2017 | 8 | Soyoun Park | KR | 58.16 | 7 | 103.20 | 8 | 161.36 | GPUSA | Female |
| 2017 | 9 | Roberta Rodeghiero | IT | 52.62 | 9 | 96.51 | 10 | 149.13 | GPUSA | Female |
| 2017 | 10 | Kanako Murakami | JP | 47.87 | 10 | 97.16 | 9 | 145.03 | GPUSA | Female |
| 2017 | 11 | Angelina Kuchvalska | LV | 47.80 | 11 | 87.17 | 11 | 134.97 | GPUSA | Female |
| 2017 | 1 | Shoma Uno | JP | 89.15 | 1 | 190.19 | 1 | 279.34 | GPUSA | Male |
| 2017 | 2 | Jason Brown | US | 85.75 | 3 | 182.63 | 2 | 268.38 | GPUSA | Male |
| 2017 | 3 | Adam Rippon | US | 87.32 | 2 | 174.11 | 3 | 261.43 | GPUSA | Male |
| 2017 | 4 | Sergei Voronov | RU | 78.68 | 5 | 166.60 | 5 | 245.28 | GPUSA | Male |
| 2017 | 5 | Boyang Jin | CN | 72.93 | 8 | 172.15 | 4 | 245.08 | GPUSA | Male |
| 2017 | 6 | Nam Nguyen | CA | 79.62 | 4 | 159.64 | 7 | 239.26 | GPUSA | Male |
| 2017 | 7 | Maxim Kovtun | RU | 67.43 | 10 | 163.32 | 6 | 230.75 | GPUSA | Male |
| 2017 | 8 | Timothy Dolensky | US | 77.59 | 6 | 148.94 | 8 | 226.53 | GPUSA | Male |
| 2017 | 9 | Jorik Hendrickx | BE | 76.62 | 7 | 148.29 | 9 | 224.91 | GPUSA | Male |
| 2017 | 10 | Brendan Kerry | AU | 71.62 | 9 | 140.14 | 10 | 211.76 | GPUSA | Male |
I also merged all the score data from the 5 specified World Championships, adding a year variable for the year the Championships took place.
#merge worlds data
worldmerged <- dplyr::bind_rows(list(world23, world22, world19, world18, world17), .id = 'year')
#clean names
worldmerged$Skater <- gsub('[0-9]', '', worldmerged$Skater)
worldmerged$Skater <- str_squish(worldmerged$Skater)
colnames(worldmerged) <- c('year', 'worldrank', 'skater', 'nation', 'sp_score', 'sp_rank', 'fs_score', 'fs_rank', 'worldscore')
#add year variable
worldmerged <- worldmerged %>%
mutate(year = ifelse(year == 1, 2023,
ifelse(year == 2, 2022,
ifelse(year == 3, 2019,
ifelse(year == 4, 2018, 2017)))))
worldmerged %>% kable() %>%
kable_styling(full_width = F) %>%
scroll_box(width = "100%", height = "200px")
| year | worldrank | skater | nation | sp_score | sp_rank | fs_score | fs_rank | worldscore |
|---|---|---|---|---|---|---|---|---|
| 2023 | 1 | Shoma Uno | 🇯🇵 | 104.63 | 1 | 196.51 | 1 | 301.14 |
| 2023 | 2 | Junhwan Cha | 🇰🇷 | 99.64 | 3 | 196.39 | 2 | 296.03 |
| 2023 | 3 | Ilia Malinin | 🇺🇸 | 100.38 | 2 | 188.06 | 3 | 288.44 |
| 2023 | 4 | Kévin Aymoz | 🇫🇷 | 95.56 | 5 | 187.41 | 4 | 282.97 |
| 2023 | 5 | Jason Brown | 🇺🇸 | 94.17 | 6 | 185.87 | 5 | 280.04 |
| 2023 | 6 | Kazuki Tomono | 🇯🇵 | 92.68 | 7 | 180.73 | 6 | 273.41 |
| 2023 | 7 | Keegan Messing | 🇨🇦 | 98.75 | 4 | 166.41 | 11 | 265.16 |
| 2023 | 8 | Lukas Britschgi | 🇨🇭 | 86.18 | 9 | 171.16 | 9 | 257.34 |
| 2023 | 9 | Matteo Rizzo | 🇮🇹 | 79.28 | 13 | 176.76 | 7 | 256.04 |
| 2023 | 10 | Adam Siao Him Fa | 🇫🇷 | 79.78 | 12 | 173.33 | 8 | 253.11 |
| 2023 | 11 | Vladimir Litvintsev | 🇦🇿 | 82.71 | 10 | 169.05 | 10 | 251.76 |
| 2023 | 12 | Daniel Grassl | 🇮🇹 | 86.50 | 8 | 157.93 | 14 | 244.43 |
| 2023 | 13 | Deniss Vasiljevs | 🇱🇻 | 82.37 | 11 | 160.78 | 13 | 243.15 |
| 2023 | 14 | Mikhail Shaidorov | 🇰🇿 | 75.41 | 18 | 161.52 | 12 | 236.93 |
| 2023 | 15 | Sota Yamamoto | 🇯🇵 | 75.48 | 17 | 156.91 | 15 | 232.39 |
| 2023 | 16 | Mark Gorodnitsky | 🇮🇱 | 77.89 | 14 | 154.24 | 16 | 232.13 |
| 2023 | 17 | Mihhail Selevko | 🇪🇪 | 76.81 | 15 | 154.13 | 17 | 230.94 |
| 2023 | 18 | Andreas Nordebäck | 🇸🇪 | 73.45 | 20 | 150.07 | 18 | 223.52 |
| 2023 | 19 | Nikita Starostin | 🇩🇪 | 75.53 | 16 | 142.34 | 19 | 217.87 |
| 2023 | 20 | Morisi Kvitelashvili | 🇬🇪 | 73.05 | 21 | 139.27 | 20 | 212.32 |
| 2023 | 21 | Andrew Torgashev | 🇺🇸 | 71.41 | 22 | 139.18 | 21 | 210.59 |
| 2023 | 22 | Boyang Jin | 🇨🇳 | 75.04 | 19 | 129.18 | 23 | 204.22 |
| 2023 | 23 | Adam Hagara | 🇸🇰 | 70.29 | 24 | 132.97 | 22 | 203.26 |
| 2023 | 24 | Maurizio Zandrón | 🇦🇹 | 70.36 | 23 | 123.95 | 24 | 194.31 |
| 2023 | 25 | Kyrylo Marsak | 🇺🇦 | 68.60 | 25 | NA | NA | NA |
| 2023 | 26 | Conrad Orzel | 🇨🇦 | 67.65 | 26 | NA | NA | NA |
| 2023 | 27 | Tomás Guarino Sabaté | 🇪🇸 | 67.60 | 27 | NA | NA | NA |
| 2023 | 28 | Burak Demirboga | 🇹🇷 | 65.73 | 28 | NA | NA | NA |
| 2023 | 29 | Nika Egadze | 🇬🇪 | 65.17 | 29 | NA | NA | NA |
| 2023 | 30 | Alexander Zlatkov | 🇧🇬 | 62.31 | 30 | NA | NA | NA |
| 2023 | 31 | Jari Kessler | 🇭🇷 | 61.94 | 31 | NA | NA | NA |
| 2023 | 32 | Graham Newberry | 🇬🇧 | 61.70 | 32 | NA | NA | NA |
| 2023 | 33 | Vladimir Samoilov | 🇵🇱 | 61.48 | 33 | NA | NA | NA |
| 2023 | 34 | Georgiy Reshtenko | 🇨🇿 | 59.93 | 34 | NA | NA | NA |
| 2023 | 1 | Kaori Sakamoto | 🇯🇵 | 79.24 | 1 | 145.37 | 2 | 224.61 |
| 2023 | 2 | Haein Lee | 🇰🇷 | 73.62 | 2 | 147.32 | 1 | 220.94 |
| 2023 | 3 | Loena Hendrickx | 🇧🇪 | 71.94 | 5 | 138.48 | 4 | 210.42 |
| 2023 | 4 | Isabeau Levito | 🇺🇸 | 73.03 | 4 | 134.62 | 5 | 207.65 |
| 2023 | 5 | Mai Mihara | 🇯🇵 | 73.46 | 3 | 132.24 | 6 | 205.70 |
| 2023 | 6 | Chaeyeon Kim | 🇰🇷 | 64.06 | 12 | 139.45 | 3 | 203.51 |
| 2023 | 7 | Nicole Schott | 🇩🇪 | 67.29 | 7 | 130.47 | 9 | 197.76 |
| 2023 | 8 | Kimmy Repond | 🇨🇭 | 62.75 | 13 | 131.34 | 8 | 194.09 |
| 2023 | 9 | Niina Petrõkina | 🇪🇪 | 68.00 | 6 | 125.49 | 12 | 193.49 |
| 2023 | 10 | Rinka Watanabe | 🇯🇵 | 60.90 | 15 | 131.91 | 7 | 192.81 |
| 2023 | 11 | Nina Pinzarrone | 🇧🇪 | 62.04 | 14 | 129.74 | 10 | 191.78 |
| 2023 | 12 | Amber Glenn | 🇺🇸 | 65.52 | 10 | 122.81 | 14 | 188.33 |
| 2023 | 13 | Madeline Schizas | 🇨🇦 | 60.02 | 16 | 127.47 | 11 | 187.49 |
| 2023 | 14 | Anastasiia Gubanova | 🇬🇪 | 65.40 | 11 | 119.52 | 15 | 184.92 |
| 2023 | 15 | Bradie Tennell | 🇺🇸 | 66.45 | 8 | 117.69 | 16 | 184.14 |
| 2023 | 16 | Ekaterina Kurakova | 🇵🇱 | 65.69 | 9 | 115.74 | 17 | 181.43 |
| 2023 | 17 | Lara Naki Gutmann | 🇮🇹 | 55.22 | 23 | 123.21 | 13 | 178.43 |
| 2023 | 18 | Yelim Kim | 🇰🇷 | 60.02 | 17 | 114.28 | 19 | 174.30 |
| 2023 | 19 | Olga Mikutina | 🇦🇹 | 57.05 | 20 | 115.26 | 18 | 172.31 |
| 2023 | 20 | Julia Sauter | 🇷🇴 | 56.02 | 22 | 109.60 | 20 | 165.62 |
| 2023 | 21 | Janna Jyrkinen | 🇫🇮 | 56.06 | 21 | 104.85 | 21 | 160.91 |
| 2023 | 22 | Lindsay Van Zundert | 🇳🇱 | 57.56 | 19 | 101.99 | 22 | 159.55 |
| 2023 | 23 | Sofja Stepchenko | 🇱🇻 | 58.87 | 18 | 99.51 | 24 | 158.38 |
| 2023 | 24 | Alexandra Feigin | 🇧🇬 | 54.65 | 24 | 101.09 | 23 | 155.74 |
| 2023 | 25 | Lorine Schild | 🇫🇷 | 54.35 | 25 | NA | NA | NA |
| 2023 | 26 | Jade Hovine | 🇧🇪 | 54.10 | 26 | NA | NA | NA |
| 2023 | 27 | Kristen Spours | 🇬🇧 | 53.38 | 27 | NA | NA | NA |
| 2023 | 28 | Ema Doboszova | 🇸🇰 | 53.01 | 28 | NA | NA | NA |
| 2023 | 29 | Kristina Isaev | 🇩🇪 | 52.93 | 29 | NA | NA | NA |
| 2023 | 30 | Anastasia Gracheva | 🇲🇩 | 50.55 | 30 | NA | NA | NA |
| 2023 | 31 | Marilena Kitromilis | 🇨🇾 | 48.92 | 31 | NA | NA | NA |
| 2023 | 32 | Eliska Brezinova | 🇨🇿 | 47.29 | 32 | NA | NA | NA |
| 2023 | 33 | Daša Grm | 🇸🇮 | 47.04 | 33 | NA | NA | NA |
| 2023 | 34 | Júlia Láng | 🇭🇺 | 44.26 | 34 | NA | NA | NA |
| 2023 | 35 | Mia Caroline Risa Gomez | 🇳🇴 | 43.54 | 35 | NA | NA | NA |
| 2022 | 1 | Shoma Uno | 🇯🇵 | 109.63 | 1 | 202.85 | 1 | 312.48 |
| 2022 | 2 | Yuma Kagiyama | 🇯🇵 | 105.69 | 2 | 191.91 | 2 | 297.60 |
| 2022 | 3 | Vincent Zhou | 🇺🇸 | 95.84 | 6 | 181.54 | 4 | 277.38 |
| 2022 | 4 | Morisi Kvitelashvili | 🇬🇪 | 92.61 | 7 | 179.42 | 5 | 272.03 |
| 2022 | 5 | Camden Pulkinen | 🇺🇸 | 89.50 | 12 | 182.19 | 3 | 271.69 |
| 2022 | 6 | Kazuki Tomono | 🇯🇵 | 101.12 | 3 | 168.25 | 8 | 269.37 |
| 2022 | 7 | Daniel Grassl | 🇮🇹 | 97.62 | 5 | 169.04 | 7 | 266.66 |
| 2022 | 8 | Adam Siao Him Fa | 🇫🇷 | 90.97 | 10 | 175.15 | 6 | 266.12 |
| 2022 | 9 | Ilia Malinin | 🇺🇸 | 100.16 | 4 | 163.63 | 11 | 263.79 |
| 2022 | 10 | Matteo Rizzo | 🇮🇹 | 91.67 | 8 | 164.08 | 10 | 255.75 |
| 2022 | 11 | Kévin Aymoz | 🇫🇷 | 85.26 | 15 | 160.20 | 12 | 245.46 |
| 2022 | 12 | Roman Sadovsky | 🇨🇦 | 80.54 | 18 | 164.82 | 9 | 245.36 |
| 2022 | 13 | Deniss Vasiljevs | 🇱🇻 | 90.95 | 11 | 152.05 | 14 | 243.00 |
| 2022 | 14 | Keegan Messing | 🇨🇦 | 91.18 | 9 | 143.85 | 17 | 235.03 |
| 2022 | 15 | Mihhail Selevko | 🇪🇪 | 78.85 | 20 | 155.87 | 13 | 234.72 |
| 2022 | 16 | Vladimir Litvintsev | 🇦🇿 | 85.83 | 14 | 147.79 | 15 | 233.62 |
| 2022 | 17 | Maurizio Zandrón | 🇦🇹 | 83.10 | 16 | 145.17 | 16 | 228.27 |
| 2022 | 18 | Sihyeong Lee | 🇰🇷 | 86.35 | 13 | 138.71 | 18 | 225.06 |
| 2022 | 19 | Nikolaj Majorov | 🇸🇪 | 79.36 | 19 | 137.09 | 20 | 216.45 |
| 2022 | 20 | Graham Newberry | 🇬🇧 | 74.92 | 21 | 135.48 | 21 | 210.40 |
| 2022 | 21 | Tomás Guarino Sabaté | 🇪🇸 | 71.42 | 24 | 137.53 | 19 | 208.95 |
| 2022 | 22 | Nikita Starostin | 🇩🇪 | 73.79 | 23 | 131.93 | 22 | 205.72 |
| 2022 | 23 | Ivan Shmuratko | 🇺🇦 | 73.99 | 22 | 122.66 | 23 | 196.65 |
| 2022 | NA | Junhwan Cha | 🇰🇷 | 82.43 | 17 | NA | NA | NA |
| 2022 | 25 | Mark Gorodnitsky | 🇮🇱 | 69.70 | 25 | NA | NA | NA |
| 2022 | 26 | Adam Hagara | 🇸🇰 | 60.92 | 26 | NA | NA | NA |
| 2022 | 27 | Vladimir Samoilov | 🇵🇱 | 60.71 | 27 | NA | NA | NA |
| 2022 | 28 | Burak Demirboga | 🇹🇷 | 52.86 | 28 | NA | NA | NA |
| 2022 | 29 | Aleksandr Vlasenko | 🇭🇺 | 51.10 | 29 | NA | NA | NA |
| 2022 | 1 | Kaori Sakamoto | 🇯🇵 | 80.32 | 1 | 155.77 | 1 | 236.09 |
| 2022 | 2 | Loena Hendrickx | 🇧🇪 | 75.00 | 2 | 142.70 | 2 | 217.70 |
| 2022 | 3 | Alysa Liu | 🇺🇸 | 71.91 | 5 | 139.28 | 3 | 211.19 |
| 2022 | 4 | Mariah Bell | 🇺🇸 | 72.55 | 3 | 136.11 | 4 | 208.66 |
| 2022 | 5 | Young You | 🇰🇷 | 72.08 | 4 | 132.83 | 6 | 204.91 |
| 2022 | 6 | Anastasiia Gubanova | 🇬🇪 | 62.59 | 14 | 134.02 | 5 | 196.61 |
| 2022 | 7 | Haein Lee | 🇰🇷 | 64.16 | 11 | 132.39 | 7 | 196.55 |
| 2022 | 8 | Karen Chen | 🇺🇸 | 66.16 | 8 | 126.35 | 8 | 192.51 |
| 2022 | 9 | Ekaterina Ryabova | 🇦🇿 | 65.52 | 9 | 122.98 | 11 | 188.50 |
| 2022 | 10 | Nicole Schott | 🇩🇪 | 67.77 | 6 | 120.65 | 14 | 188.42 |
| 2022 | 11 | Wakaba Higuchi | 🇯🇵 | 67.03 | 7 | 121.12 | 12 | 188.15 |
| 2022 | 12 | Madeline Schizas | 🇨🇦 | 64.20 | 10 | 123.94 | 10 | 188.14 |
| 2022 | 13 | Ekaterina Kurakova | 🇵🇱 | 61.92 | 16 | 124.51 | 9 | 186.43 |
| 2022 | 14 | Olga Mikutina | 🇦🇹 | 62.14 | 15 | 120.84 | 13 | 182.98 |
| 2022 | 15 | Mana Kawabe | 🇯🇵 | 63.68 | 12 | 118.76 | 15 | 182.44 |
| 2022 | 16 | Niina Petrõkina | 🇪🇪 | 60.24 | 17 | 116.36 | 16 | 176.60 |
| 2022 | 17 | Lindsay Van Zundert | 🇳🇱 | 58.49 | 18 | 112.90 | 17 | 171.39 |
| 2022 | 18 | Julia Sauter | 🇷🇴 | 58.07 | 19 | 112.24 | 18 | 170.31 |
| 2022 | 19 | Alexia Paganini | 🇨🇭 | 63.09 | 13 | 106.93 | 19 | 170.02 |
| 2022 | 20 | Lara Naki Gutmann | 🇮🇹 | 57.92 | 20 | 106.47 | 20 | 164.39 |
| 2022 | 21 | Josefin Taljegård | 🇸🇪 | 57.52 | 21 | 105.72 | 21 | 163.24 |
| 2022 | 22 | Kailani Craine | 🇦🇺 | 56.64 | 22 | 105.11 | 22 | 161.75 |
| 2022 | 23 | Natasha Mckay | 🇬🇧 | 55.71 | 24 | 103.56 | 23 | 159.27 |
| 2022 | 24 | Daša Grm | 🇸🇮 | 55.82 | 23 | 91.30 | 24 | 147.12 |
| 2022 | 25 | Jenni Saarinen | 🇫🇮 | 55.30 | 25 | NA | NA | NA |
| 2022 | 26 | Tzu-Han Ting | 🇹🇼 | 55.24 | 26 | NA | NA | NA |
| 2022 | 27 | Eliska Brezinova | 🇨🇿 | 55.07 | 27 | NA | NA | NA |
| 2022 | 28 | Alexandra Feigin | 🇧🇬 | 55.01 | 28 | NA | NA | NA |
| 2022 | 29 | Léa Serna | 🇫🇷 | 54.30 | 29 | NA | NA | NA |
| 2022 | 30 | Marilena Kitromilis | 🇨🇾 | 53.32 | 30 | NA | NA | NA |
| 2022 | 31 | Júlia Láng | 🇭🇺 | 47.93 | 31 | NA | NA | NA |
| 2022 | 32 | Stefanie Pesendorfer | 🇦🇹 | 47.23 | 32 | NA | NA | NA |
| 2022 | 33 | Anete Lace | 🇱🇻 | 44.60 | 33 | NA | NA | NA |
| 2019 | 1 | Nathan Chen | 🇺🇸 | 107.40 | 1 | 216.02 | 1 | 323.42 |
| 2019 | 2 | Yuzuru Hanyu | 🇯🇵 | 94.87 | 3 | 206.10 | 2 | 300.97 |
| 2019 | 3 | Vincent Zhou | 🇺🇸 | 94.17 | 4 | 186.99 | 3 | 281.16 |
| 2019 | 4 | Shoma Uno | 🇯🇵 | 91.40 | 6 | 178.92 | 4 | 270.32 |
| 2019 | 5 | Boyang Jin | 🇨🇳 | 84.26 | 9 | 178.45 | 5 | 262.71 |
| 2019 | 6 | Mikhail Kolyada | 🇷🇺 | 84.23 | 10 | 178.21 | 6 | 262.44 |
| 2019 | 7 | Matteo Rizzo | 🇮🇹 | 93.37 | 5 | 164.29 | 10 | 257.66 |
| 2019 | 8 | Michal Březina | 🇨🇿 | 86.96 | 8 | 167.32 | 8 | 254.28 |
| 2019 | 9 | Jason Brown | 🇺🇸 | 96.81 | 2 | 157.34 | 14 | 254.15 |
| 2019 | 10 | Andrei Lazukin | 🇷🇺 | 84.05 | 11 | 164.69 | 9 | 248.74 |
| 2019 | 11 | Kévin Aymoz | 🇫🇷 | 88.24 | 7 | 159.23 | 12 | 247.47 |
| 2019 | 12 | Alexander Samarin | 🇷🇺 | 78.38 | 20 | 167.95 | 7 | 246.33 |
| 2019 | 13 | Morisi Kvitelashvili | 🇬🇪 | 82.67 | 12 | 158.07 | 13 | 240.74 |
| 2019 | 14 | Keiji Tanaka | 🇯🇵 | 78.76 | 19 | 159.64 | 11 | 238.40 |
| 2019 | 15 | Keegan Messing | 🇨🇦 | 82.38 | 14 | 155.26 | 15 | 237.64 |
| 2019 | 16 | Nam Nguyen | 🇨🇦 | 82.51 | 13 | 154.76 | 16 | 237.27 |
| 2019 | 17 | Vladimir Litvintsev | 🇦🇿 | 81.46 | 16 | 149.38 | 19 | 230.84 |
| 2019 | 18 | Alexander Majorov | 🇸🇪 | 79.17 | 17 | 150.55 | 17 | 229.72 |
| 2019 | 19 | Junhwan Cha | 🇰🇷 | 79.17 | 18 | 150.09 | 18 | 229.26 |
| 2019 | 20 | Brendan Kerry | 🇦🇺 | 78.26 | 21 | 143.76 | 21 | 222.02 |
| 2019 | 21 | Deniss Vasiljevs | 🇱🇻 | 74.74 | 23 | 143.78 | 20 | 218.52 |
| 2019 | 22 | Alexei Bychenko | 🇮🇱 | 77.67 | 22 | 138.93 | 22 | 216.60 |
| 2019 | 23 | Julian Zhi-Jie Yee | 🇲🇾 | 73.63 | 24 | 132.34 | 23 | 205.97 |
| 2019 | 24 | Daniel Samohin | 🇮🇱 | 82.00 | 15 | 123.28 | 24 | 205.28 |
| 2019 | 25 | Peter James Hallam | 🇬🇧 | 66.06 | 25 | NA | NA | NA |
| 2019 | 26 | Luc Maierhofer | 🇦🇹 | 65.78 | 26 | NA | NA | NA |
| 2019 | 27 | Aleksandr Selevko | 🇪🇪 | 63.25 | 27 | NA | NA | NA |
| 2019 | 28 | Paul Fentz | 🇩🇪 | 63.24 | 28 | NA | NA | NA |
| 2019 | 29 | Ivan Shmuratko | 🇺🇦 | 62.99 | 29 | NA | NA | NA |
| 2019 | 30 | Burak Demirboga | 🇹🇷 | 60.79 | 30 | NA | NA | NA |
| 2019 | 31 | Slavik Hayrapetyan | 🇦🇲 | 60.66 | 31 | NA | NA | NA |
| 2019 | 32 | Valtter Virtanen | 🇫🇮 | 55.73 | 32 | NA | NA | NA |
| 2019 | 33 | Donovan Carrillo | 🇲🇽 | 54.99 | 33 | NA | NA | NA |
| 2019 | 34 | Lukas Britschgi | 🇨🇭 | 54.58 | 34 | NA | NA | NA |
| 2019 | 35 | Ihor Reznichenko | 🇵🇱 | 50.15 | 35 | NA | NA | NA |
| 2019 | 1 | Alina Zagitova | 🇷🇺 | 82.08 | 1 | 155.42 | 1 | 237.50 |
| 2019 | 2 | Elizabet Tursynbayeva | 🇰🇿 | 75.96 | 3 | 148.80 | 4 | 224.76 |
| 2019 | 3 | Evgenia Medvedeva | 🇷🇺 | 74.23 | 4 | 149.57 | 3 | 223.80 |
| 2019 | 4 | Rika Kihira | 🇯🇵 | 70.90 | 7 | 152.59 | 2 | 223.49 |
| 2019 | 5 | Kaori Sakamoto | 🇯🇵 | 76.86 | 2 | 145.97 | 5 | 222.83 |
| 2019 | 6 | Satoko Miyahara | 🇯🇵 | 70.60 | 8 | 145.35 | 6 | 215.95 |
| 2019 | 7 | Bradie Tennell | 🇺🇸 | 69.50 | 10 | 143.97 | 7 | 213.47 |
| 2019 | 8 | Sofia Samodurova | 🇷🇺 | 70.42 | 9 | 138.16 | 8 | 208.58 |
| 2019 | 9 | Mariah Bell | 🇺🇸 | 71.26 | 6 | 136.81 | 9 | 208.07 |
| 2019 | 10 | Eunsoo Lim | 🇰🇷 | 72.91 | 5 | 132.66 | 10 | 205.57 |
| 2019 | 11 | Gabrielle Daleman | 🇨🇦 | 69.19 | 11 | 123.48 | 12 | 192.67 |
| 2019 | 12 | Loena Hendrickx | 🇧🇪 | 62.60 | 13 | 123.69 | 11 | 186.29 |
| 2019 | 13 | Ekaterina Ryabova | 🇦🇿 | 57.18 | 17 | 122.70 | 13 | 179.88 |
| 2019 | 14 | Yi Christy Leung | 🇭🇰 | 58.60 | 14 | 118.62 | 14 | 177.22 |
| 2019 | 15 | Laurine Lecavelier | 🇫🇷 | 56.81 | 19 | 113.78 | 15 | 170.59 |
| 2019 | 16 | Nicole Schott | 🇩🇪 | 63.18 | 12 | 107.38 | 17 | 170.56 |
| 2019 | 17 | Alexandra Feigin | 🇧🇬 | 56.69 | 20 | 108.62 | 16 | 165.31 |
| 2019 | 18 | Daša Grm | 🇸🇮 | 57.58 | 16 | 103.58 | 18 | 161.16 |
| 2019 | 19 | Hongyi Chen | 🇨🇳 | 58.53 | 15 | 99.06 | 19 | 157.59 |
| 2019 | 20 | Eliska Brezinova | 🇨🇿 | 57.13 | 18 | 96.32 | 20 | 153.45 |
| 2019 | 21 | Natasha Mckay | 🇬🇧 | 56.40 | 21 | 95.16 | 21 | 151.56 |
| 2019 | 22 | Eva Lotta Kiibus | 🇪🇪 | 55.38 | 23 | 94.61 | 22 | 149.99 |
| 2019 | 23 | Alaine Chartrand | 🇨🇦 | 55.89 | 22 | 93.08 | 23 | 148.97 |
| 2019 | 24 | Isadora Williams | 🇧🇷 | 55.20 | 24 | 88.02 | 24 | 143.22 |
| 2019 | 25 | Ivett Tóth | 🇭🇺 | 54.87 | 25 | NA | NA | NA |
| 2019 | 26 | Pernille Sørensen | 🇩🇰 | 54.36 | 26 | NA | NA | NA |
| 2019 | 27 | Marina Piredda | 🇮🇹 | 53.27 | 27 | NA | NA | NA |
| 2019 | 28 | Emmi Peltonen | 🇫🇮 | 53.22 | 28 | NA | NA | NA |
| 2019 | 29 | Julia Sauter | 🇷🇴 | 53.11 | 29 | NA | NA | NA |
| 2019 | 30 | Anita Östlund | 🇸🇪 | 53.07 | 30 | NA | NA | NA |
| 2019 | 31 | Roberta Rodeghiero | 🇮🇹 | 51.50 | 31 | NA | NA | NA |
| 2019 | 32 | Nicole Rajičová | 🇸🇰 | 51.22 | 32 | NA | NA | NA |
| 2019 | 33 | Alexia Paganini | 🇨🇭 | 50.51 | 33 | NA | NA | NA |
| 2019 | 34 | Valentina Matos | 🇪🇸 | 50.25 | 34 | NA | NA | NA |
| 2019 | 35 | Aurora Cotop | 🇨🇦 | 48.83 | 35 | NA | NA | NA |
| 2019 | 36 | Kailani Craine | 🇦🇺 | 48.82 | 36 | NA | NA | NA |
| 2019 | 37 | Sophia Schaller | 🇦🇹 | 48.72 | 37 | NA | NA | NA |
| 2019 | 38 | Elzbieta Kropa | 🇱🇹 | 47.95 | 38 | NA | NA | NA |
| 2019 | 39 | Anastasiya Galustyan | 🇦🇲 | 47.75 | 39 | NA | NA | NA |
| 2019 | 40 | Kyarha Van Tiel | 🇳🇱 | 41.85 | 40 | NA | NA | NA |
| 2018 | 1 | Nathan Chen | 🇺🇸 | 101.94 | 1 | 219.46 | 1 | 321.40 |
| 2018 | 2 | Shoma Uno | 🇯🇵 | 94.26 | 5 | 179.51 | 2 | 273.77 |
| 2018 | 3 | Mikhail Kolyada | 🇷🇺 | 100.08 | 2 | 172.24 | 4 | 272.32 |
| 2018 | 4 | Alexei Bychenko | 🇮🇱 | 90.99 | 7 | 167.29 | 7 | 258.28 |
| 2018 | 5 | Kazuki Tomono | 🇯🇵 | 82.61 | 11 | 173.50 | 3 | 256.11 |
| 2018 | 6 | Deniss Vasiljevs | 🇱🇻 | 84.25 | 9 | 170.61 | 5 | 254.86 |
| 2018 | 7 | Dmitri Aliev | 🇷🇺 | 82.15 | 13 | 170.15 | 6 | 252.30 |
| 2018 | 8 | Keegan Messing | 🇨🇦 | 93.00 | 6 | 159.30 | 11 | 252.30 |
| 2018 | 9 | Misha Ge | 🇺🇿 | 86.01 | 8 | 163.56 | 9 | 249.57 |
| 2018 | 10 | Michal Březina | 🇨🇿 | 78.01 | 17 | 165.98 | 8 | 243.99 |
| 2018 | 11 | Max Aaron | 🇺🇸 | 79.78 | 15 | 161.71 | 10 | 241.49 |
| 2018 | 12 | Alexander Majorov | 🇸🇪 | 82.71 | 10 | 155.08 | 13 | 237.79 |
| 2018 | 13 | Keiji Tanaka | 🇯🇵 | 80.17 | 14 | 156.49 | 12 | 236.66 |
| 2018 | 14 | Vincent Zhou | 🇺🇸 | 96.78 | 3 | 138.46 | 19 | 235.24 |
| 2018 | 15 | Paul Fentz | 🇩🇪 | 82.49 | 12 | 148.43 | 16 | 230.92 |
| 2018 | 16 | Romain Ponsart | 🇫🇷 | 79.55 | 16 | 149.65 | 14 | 229.20 |
| 2018 | 17 | Matteo Rizzo | 🇮🇹 | 77.43 | 18 | 148.01 | 17 | 225.44 |
| 2018 | 18 | Brendan Kerry | 🇦🇺 | 74.99 | 19 | 148.86 | 15 | 223.85 |
| 2018 | 19 | Boyang Jin | 🇨🇳 | 95.85 | 4 | 127.56 | 23 | 223.41 |
| 2018 | 20 | Daniel Samohin | 🇮🇱 | 72.78 | 20 | 141.23 | 18 | 214.01 |
| 2018 | 21 | Julian Zhi-Jie Yee | 🇲🇾 | 72.43 | 21 | 136.60 | 20 | 209.03 |
| 2018 | 22 | Donovan Carrillo | 🇲🇽 | 68.13 | 24 | 132.63 | 21 | 200.76 |
| 2018 | 23 | Slavik Hayrapetyan | 🇦🇲 | 68.18 | 23 | 131.54 | 22 | 199.72 |
| 2018 | 24 | Phillip Harris | 🇬🇧 | 68.59 | 22 | 119.10 | 24 | 187.69 |
| 2018 | 25 | Nam Nguyen | 🇨🇦 | 67.79 | 25 | NA | NA | NA |
| 2018 | 26 | Morisi Kvitelashvili | 🇬🇪 | 67.01 | 26 | NA | NA | NA |
| 2018 | 27 | Stéphane Walker | 🇨🇭 | 65.79 | 27 | NA | NA | NA |
| 2018 | 28 | Burak Demirboga | 🇹🇷 | 65.43 | 28 | NA | NA | NA |
| 2018 | 29 | Ivan Pavlov | 🇺🇦 | 64.18 | 29 | NA | NA | NA |
| 2018 | 30 | Chih-I Tsao | 🇹🇼 | 64.06 | 30 | NA | NA | NA |
| 2018 | 31 | Larry Loupolover | 🇦🇿 | 61.82 | 31 | NA | NA | NA |
| 2018 | 32 | Abzal Rakimgaliev | 🇰🇿 | 61.19 | 32 | NA | NA | NA |
| 2018 | 33 | Jinseo Kim | 🇰🇷 | 60.72 | 33 | NA | NA | NA |
| 2018 | 34 | Nicholas Vrdoljak | 🇭🇷 | 59.74 | 34 | NA | NA | NA |
| 2018 | 35 | Valtter Virtanen | 🇫🇮 | 55.49 | 35 | NA | NA | NA |
| 2018 | 36 | Ihor Reznichenko | 🇵🇱 | 51.70 | 36 | NA | NA | NA |
| 2018 | 37 | Javier Raya | 🇪🇸 | 50.00 | 37 | NA | NA | NA |
| 2018 | 1 | Kaetlyn Osmond | 🇨🇦 | 72.73 | 4 | 150.50 | 1 | 223.23 |
| 2018 | 2 | Wakaba Higuchi | 🇯🇵 | 65.89 | 8 | 145.01 | 2 | 210.90 |
| 2018 | 3 | Satoko Miyahara | 🇯🇵 | 74.36 | 3 | 135.72 | 3 | 210.08 |
| 2018 | 4 | Carolina Kostner | 🇮🇹 | 80.27 | 1 | 128.61 | 5 | 208.88 |
| 2018 | 5 | Alina Zagitova | 🇷🇺 | 79.51 | 2 | 128.21 | 7 | 207.72 |
| 2018 | 6 | Bradie Tennell | 🇺🇸 | 68.76 | 7 | 131.13 | 4 | 199.89 |
| 2018 | 7 | Gabrielle Daleman | 🇨🇦 | 71.61 | 6 | 125.11 | 8 | 196.72 |
| 2018 | 8 | Maria Sotskova | 🇷🇺 | 71.80 | 5 | 124.81 | 9 | 196.61 |
| 2018 | 9 | Loena Hendrickx | 🇧🇪 | 64.07 | 10 | 128.24 | 6 | 192.31 |
| 2018 | 10 | Mirai Nagasu | 🇺🇸 | 65.21 | 9 | 122.31 | 11 | 187.52 |
| 2018 | 11 | Elizabet Tursynbayeva | 🇰🇿 | 62.38 | 11 | 124.47 | 10 | 186.85 |
| 2018 | 12 | Mariah Bell | 🇺🇸 | 59.15 | 17 | 115.25 | 12 | 174.40 |
| 2018 | 13 | Nicole Schott | 🇩🇪 | 61.84 | 12 | 112.29 | 14 | 174.13 |
| 2018 | 14 | Laurine Lecavelier | 🇫🇷 | 59.79 | 15 | 113.44 | 13 | 173.23 |
| 2018 | 15 | Hanul Kim | 🇰🇷 | 60.14 | 14 | 110.54 | 15 | 170.68 |
| 2018 | 16 | Viveca Lindfors | 🇫🇮 | 60.18 | 13 | 106.05 | 16 | 166.23 |
| 2018 | 17 | Kailani Craine | 🇦🇺 | 56.90 | 20 | 97.51 | 18 | 154.41 |
| 2018 | 18 | Eliska Brezinova | 🇨🇿 | 58.37 | 18 | 94.77 | 19 | 153.14 |
| 2018 | 19 | Stanislava Konstantinova | 🇷🇺 | 59.19 | 16 | 93.84 | 20 | 153.03 |
| 2018 | 20 | Alexia Paganini | 🇨🇭 | 57.86 | 19 | 91.80 | 22 | 149.66 |
| 2018 | 21 | Elisabetta Leccardi | 🇮🇹 | 51.13 | 23 | 98.04 | 17 | 149.17 |
| 2018 | 22 | Daša Grm | 🇸🇮 | 52.43 | 22 | 92.08 | 21 | 144.51 |
| 2018 | 23 | Ivett Tóth | 🇭🇺 | 50.63 | 24 | 86.24 | 23 | 136.87 |
| 2018 | NA | Dabin Choi | 🇰🇷 | 55.30 | 21 | NA | NA | NA |
| 2018 | 25 | Larkyn Austman | 🇨🇦 | 50.17 | 25 | NA | NA | NA |
| 2018 | 26 | Xiangning Li | 🇨🇳 | 50.06 | 26 | NA | NA | NA |
| 2018 | 27 | Nicole Rajičová | 🇸🇰 | 49.87 | 27 | NA | NA | NA |
| 2018 | 28 | Amy Lin | 🇹🇼 | 49.31 | 28 | NA | NA | NA |
| 2018 | 29 | Anita Östlund | 🇸🇪 | 48.99 | 29 | NA | NA | NA |
| 2018 | 30 | Alisa Stomakhina | 🇦🇹 | 48.71 | 30 | NA | NA | NA |
| 2018 | 31 | Elzbieta Kropa | 🇱🇹 | 46.53 | 31 | NA | NA | NA |
| 2018 | 32 | Natasha Mckay | 🇬🇧 | 45.89 | 32 | NA | NA | NA |
| 2018 | 33 | Anne Line Gjersem | 🇳🇴 | 45.25 | 33 | NA | NA | NA |
| 2018 | 34 | Gerli Liinamäe | 🇪🇪 | 45.14 | 34 | NA | NA | NA |
| 2018 | 35 | Isadora Williams | 🇧🇷 | 42.16 | 35 | NA | NA | NA |
| 2018 | 36 | Antonina Dubinina | 🇷🇸 | 41.40 | 36 | NA | NA | NA |
| 2018 | 37 | Angelina Kuchvalska | 🇱🇻 | 35.78 | 37 | NA | NA | NA |
| 2017 | 1 | Yuzuru Hanyu | 🇯🇵 | 98.39 | 5 | 223.20 | 1 | 321.59 |
| 2017 | 2 | Shoma Uno | 🇯🇵 | 104.86 | 2 | 214.45 | 2 | 319.31 |
| 2017 | 3 | Boyang Jin | 🇨🇳 | 98.64 | 4 | 204.94 | 3 | 303.58 |
| 2017 | 4 | Javier Fernández | 🇪🇸 | 109.05 | 1 | 192.14 | 6 | 301.19 |
| 2017 | 5 | Patrick Chan | 🇨🇦 | 102.13 | 3 | 193.03 | 5 | 295.16 |
| 2017 | 6 | Nathan Chen | 🇺🇸 | 97.33 | 6 | 193.39 | 4 | 290.72 |
| 2017 | 7 | Jason Brown | 🇺🇸 | 93.10 | 8 | 176.47 | 7 | 269.57 |
| 2017 | 8 | Mikhail Kolyada | 🇷🇺 | 93.28 | 7 | 164.19 | 9 | 257.47 |
| 2017 | 9 | Kevin Reynolds | 🇨🇦 | 84.44 | 12 | 169.40 | 8 | 253.84 |
| 2017 | 10 | Alexei Bychenko | 🇮🇱 | 85.28 | 11 | 160.68 | 12 | 245.96 |
| 2017 | 11 | Maxim Kovtun | 🇷🇺 | 89.38 | 10 | 156.46 | 14 | 245.84 |
| 2017 | 12 | Misha Ge | 🇺🇿 | 79.91 | 16 | 163.54 | 10 | 243.45 |
| 2017 | 13 | Morisi Kvitelashvili | 🇬🇪 | 76.34 | 19 | 162.90 | 11 | 239.24 |
| 2017 | 14 | Deniss Vasiljevs | 🇱🇻 | 81.73 | 14 | 157.27 | 13 | 239.00 |
| 2017 | 15 | Brendan Kerry | 🇦🇺 | 83.11 | 13 | 153.13 | 15 | 236.24 |
| 2017 | 16 | Denis Ten | 🇰🇿 | 90.18 | 9 | 144.13 | 20 | 234.31 |
| 2017 | 17 | Chafik Besseghier | 🇫🇷 | 78.82 | 17 | 151.31 | 16 | 230.13 |
| 2017 | 18 | Michal Březina | 🇨🇿 | 80.02 | 15 | 146.24 | 18 | 226.26 |
| 2017 | 19 | Keiji Tanaka | 🇯🇵 | 73.45 | 22 | 148.89 | 17 | 222.34 |
| 2017 | 20 | Paul Fentz | 🇩🇪 | 73.89 | 20 | 144.02 | 21 | 217.91 |
| 2017 | 21 | Jorik Hendrickx | 🇧🇪 | 73.68 | 21 | 140.34 | 22 | 214.02 |
| 2017 | 22 | Julian Zhi-Jie Yee | 🇲🇾 | 69.74 | 23 | 144.25 | 19 | 213.99 |
| 2017 | 23 | Alexander Majorov | 🇸🇪 | 77.23 | 18 | 127.81 | 23 | 205.04 |
| 2017 | 24 | Michael Christian Martinez | 🇵🇭 | 69.32 | 24 | 127.47 | 24 | 196.79 |
| 2017 | 25 | Ivan Pavlov | 🇺🇦 | 69.26 | 25 | NA | NA | NA |
| 2017 | 26 | Jinseo Kim | 🇰🇷 | 68.66 | 26 | NA | NA | NA |
| 2017 | 27 | Javier Raya | 🇪🇸 | 66.88 | 27 | NA | NA | NA |
| 2017 | 28 | Stéphane Walker | 🇨🇭 | 64.04 | 28 | NA | NA | NA |
| 2017 | 29 | Ihor Reznichenko | 🇵🇱 | 63.88 | 29 | NA | NA | NA |
| 2017 | 30 | Matteo Rizzo | 🇮🇹 | 63.14 | 30 | NA | NA | NA |
| 2017 | 31 | Graham Newberry | 🇬🇧 | 62.04 | 31 | NA | NA | NA |
| 2017 | 32 | Chih-I Tsao | 🇹🇼 | 61.52 | 32 | NA | NA | NA |
| 2017 | 33 | Valtter Virtanen | 🇫🇮 | 59.45 | 33 | NA | NA | NA |
| 2017 | 34 | Nicholas Vrdoljak | 🇭🇷 | 57.28 | 34 | NA | NA | NA |
| 2017 | 35 | Slavik Hayrapetyan | 🇦🇲 | 57.14 | 35 | NA | NA | NA |
| 2017 | 36 | Larry Loupolover | 🇦🇿 | 38.97 | 36 | NA | NA | NA |
| 2017 | 1 | Evgenia Medvedeva | 🇷🇺 | 79.01 | 1 | 154.40 | 1 | 233.41 |
| 2017 | 2 | Kaetlyn Osmond | 🇨🇦 | 75.98 | 2 | 142.15 | 2 | 218.13 |
| 2017 | 3 | Gabrielle Daleman | 🇨🇦 | 72.19 | 3 | 141.33 | 3 | 213.52 |
| 2017 | 4 | Karen Chen | 🇺🇸 | 69.98 | 5 | 129.31 | 6 | 199.29 |
| 2017 | 5 | Mai Mihara | 🇯🇵 | 59.59 | 15 | 138.29 | 4 | 197.88 |
| 2017 | 6 | Carolina Kostner | 🇮🇹 | 66.33 | 8 | 130.50 | 5 | 196.83 |
| 2017 | 7 | Ashley Wagner | 🇺🇸 | 69.04 | 7 | 124.50 | 10 | 193.54 |
| 2017 | 8 | Maria Sotskova | 🇷🇺 | 69.76 | 6 | 122.44 | 11 | 192.20 |
| 2017 | 9 | Elizabet Tursynbayeva | 🇰🇿 | 65.48 | 10 | 126.51 | 8 | 191.99 |
| 2017 | 10 | Dabin Choi | 🇰🇷 | 62.66 | 11 | 128.45 | 7 | 191.11 |
| 2017 | 11 | Wakaba Higuchi | 🇯🇵 | 65.87 | 9 | 122.18 | 12 | 188.05 |
| 2017 | 12 | Mariah Bell | 🇺🇸 | 61.02 | 13 | 126.21 | 9 | 187.23 |
| 2017 | 13 | Anna Pogorilaya | 🇷🇺 | 71.52 | 4 | 111.85 | 15 | 183.37 |
| 2017 | 14 | Xiangning Li | 🇨🇳 | 58.28 | 16 | 117.09 | 13 | 175.37 |
| 2017 | 15 | Loena Hendrickx | 🇧🇪 | 57.54 | 17 | 115.28 | 14 | 172.82 |
| 2017 | 16 | Rika Hongo | 🇯🇵 | 62.55 | 12 | 107.28 | 18 | 169.83 |
| 2017 | 17 | Nicole Rajičová | 🇸🇰 | 57.08 | 18 | 108.47 | 16 | 165.55 |
| 2017 | 18 | Laurine Lecavelier | 🇫🇷 | 55.49 | 22 | 107.50 | 17 | 162.99 |
| 2017 | 19 | Nicole Schott | 🇩🇪 | 54.83 | 24 | 106.58 | 19 | 161.41 |
| 2017 | 20 | Ivett Tóth | 🇭🇺 | 61.00 | 14 | 99.77 | 21 | 160.77 |
| 2017 | 21 | Zijun Li | 🇨🇳 | 56.30 | 20 | 103.50 | 20 | 159.80 |
| 2017 | 22 | Angelina Kuchvalska | 🇱🇻 | 55.92 | 21 | 99.10 | 22 | 155.02 |
| 2017 | 23 | Anastasiya Galustyan | 🇦🇲 | 55.20 | 23 | 98.27 | 23 | 153.47 |
| 2017 | 24 | Kailani Craine | 🇦🇺 | 56.97 | 19 | 95.97 | 24 | 152.94 |
| 2017 | 25 | Shuran Yu | 🇸🇬 | 52.87 | 25 | NA | NA | NA |
| 2017 | 26 | Joshi Helgesson | 🇸🇪 | 52.07 | 26 | NA | NA | NA |
| 2017 | 27 | Helery Hälvin | 🇪🇪 | 51.94 | 27 | NA | NA | NA |
| 2017 | 28 | Amy Lin | 🇹🇼 | 51.86 | 28 | NA | NA | NA |
| 2017 | 29 | Emmi Peltonen | 🇫🇮 | 50.74 | 29 | NA | NA | NA |
| 2017 | 30 | Isadora Williams | 🇧🇷 | 50.65 | 30 | NA | NA | NA |
| 2017 | 31 | Kerstin Frank | 🇦🇹 | 50.54 | 31 | NA | NA | NA |
| 2017 | 32 | Natasha Mckay | 🇬🇧 | 50.10 | 32 | NA | NA | NA |
| 2017 | 33 | Yasmine Kimiko Yamada | 🇨🇭 | 47.86 | 33 | NA | NA | NA |
| 2017 | 34 | Anne Line Gjersem | 🇳🇴 | 46.99 | 34 | NA | NA | NA |
| 2017 | 35 | Anna Khnychenkova | 🇺🇦 | 46.98 | 35 | NA | NA | NA |
| 2017 | 36 | Daša Grm | 🇸🇮 | 46.63 | 36 | NA | NA | NA |
| 2017 | 37 | Michaela Hanzlikova | 🇨🇿 | 32.21 | 37 | NA | NA | NA |
Let’s explore if there are any missing data in our datasets first. Here is a missing data plot for our regular competition score dataset:
reg_merged %>%
vis_miss()
And for the Worlds dataset:
worldmerged %>%
vis_miss()
There seems to be a lot of missing data within the Free Skate score and rank, and the total score and final rank. Some of this could be because at bigger events, like the European Championships, 4 Continents championships, and Worlds Championships, only the top 24 skaters can advanced to the free skate. This leaves them with no free skate score or rank, and also doesn’t count towards their total score since they technically did not finish the competition. Here is an example below from the 2023 European Championships for women, where those ranked below 24 have missing data.
| year | final_rank | skater | nation | sp_score | sp_rank | fs_score | fs_rank | total_score | competition | sex |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023 | 25 | Alexandra Michaela Filcová | SK | 43.94 | 25 | NA | NA | NA | EC | Female |
| 2023 | 26 | Léa Serna | FR | 43.93 | 26 | NA | NA | NA | EC | Female |
| 2023 | 27 | Antonina Dubinina | RS | 42.51 | 27 | NA | NA | NA | EC | Female |
| 2023 | 28 | Anastasia Gracheva | MD | 39.08 | 28 | NA | NA | NA | EC | Female |
| 2023 | 29 | Alexandra Mintsidou | GR | 33.86 | 29 | NA | NA | NA | EC | Female |
A missing final rank could be a skater withdrawing from the competition. An example would be Isabeau Levito during 2023 4 Continents. She did her short program but withdrew after because of sickness and therefore does not get ranked.
reg_merged %>%
filter(skater == 'Isabeau Levito', competition == '4CC', year == 2023)%>%
kable() %>%
kable_styling(full_width = F)
| year | final_rank | skater | nation | sp_score | sp_rank | fs_score | fs_rank | total_score | competition | sex |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023 | NA | Isabeau Levito | US | 71.5 | 2 | NA | NA | NA | 4CC | Female |
For the dataset of Worlds score, we see the same issues for missing data; however, I filled in their total score with their short program score if they reached that point since we need it for total World score as the response variable. We can say the free skate score was 0 since they did not get to do it, so their total score is just their short program score and they are ranked based on that. We can see this below with how the Worlds score between 24th and 25th place has a huge drop.
#fix missing total score
worldmerged$worldscore <- ifelse(is.na(worldmerged$worldscore), worldmerged$sp_score, worldmerged$worldscore)
#example
worldmerged %>%
filter(year == 2018, worldrank <=25, worldrank>=24, skater != "Larkyn Austman") %>%
kable() %>%
kable_styling(full_width = F) %>%
scroll_box(width = "100%", height = "200px")
| year | worldrank | skater | nation | sp_score | sp_rank | fs_score | fs_rank | worldscore |
|---|---|---|---|---|---|---|---|---|
| 2018 | 24 | Phillip Harris | 🇬🇧 | 68.59 | 22 | 119.1 | 24 | 187.69 |
| 2018 | 25 | Nam Nguyen | 🇨🇦 | 67.79 | 25 | NA | NA | 67.79 |
First, I decided that I would separate average short program score and average free skate score, rather than just using average total score, for my analysis. This is because sometime skaters can do better in one program compared to the other. Free skates allow for higher scores to be earned, so if a skater typically falters during a free skate, we would like to see that reflected. It also helps our missing data problem, where any missing free skate scores won’t have too much of an effect as long as the skater has another free skate score in the season or the skater doesn’t reach the free skate during Worlds.
So I added 2 columns into the regular competition score data set that holds the average scores for each skater for that season. Then I pivoted the dataframe so that each competition has its own column and is marked 1 if the skater attended the event that season and a 0 if not. Finally, I merged the regular score dataframe with the Worlds score dataframe, dropped unneeded columns, and finally got for each season, each skater has one row with their name, the ending year of the season as a factor, their sex, their short program average, their free program average, factor variables that tells us which competition they went to in the season, and for the response variable for our predictions: their score at Worlds that year.
#take averages
avg <- reg_merged %>%
group_by(skater, year) %>%
mutate(spavg = mean(sp_score,na.rm = TRUE)) %>%
mutate(fpavg = mean(fs_score, na.rm=TRUE)) %>%
mutate(tsavg = mean(total_score, na.rm=TRUE)) %>%
select(skater, year, competition, spavg, fpavg, tsavg, sex)
#pivot competition variable
avg$truth <- 1
pivoted <- avg %>%
pivot_wider(names_from = competition, values_from = truth)
pivoted[is.na(pivoted)] <- 0
#drop unneeded columns in worldmerged
worldmerged <- worldmerged %>%
select(year, skater, worldscore, worldrank)
#for graphing later on
graphdata <- merge(worldmerged, pivoted, by = c('skater', 'year'))
graphdata$tsavg <- ifelse(graphdata$tsavg==0, graphdata$spavg, graphdata$tsavg)
#final dataframe for analysis
data <- graphdata %>%
select(-tsavg, - worldrank) %>%
mutate(year = as.factor(year))
#factor competition variables
for (i in c(7:19)) {
data[, i] <- factor(data[, i])
}
data %>% kable() %>%
kable_styling(full_width = F) %>%
scroll_box(width = "100%", height = "200px")
| skater | year | worldscore | spavg | fpavg | sex | GPUSA | GPJPN | GPGBR | GPFRA | GPFIN | GPF | GPCAN | EC | 4CC | OLY | GPRUS | GPITA | GPCHN |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Abzal Rakimgaliev | 2018 | 61.19 | 60.77000 | 114.81000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Adam Hagara | 2022 | 60.92 | 65.23000 | 0.00000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Adam Hagara | 2023 | 203.26 | 65.15000 | 124.57000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Adam Siao Him Fa | 2022 | 266.12 | 79.60333 | 157.38333 | Male | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| Adam Siao Him Fa | 2023 | 253.11 | 90.65667 | 171.74333 | Male | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Alaine Chartrand | 2019 | 148.97 | 51.11667 | 107.28333 | Female | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| Aleksandr Selevko | 2019 | 63.25 | 69.94000 | 125.19000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Alexander Majorov | 2017 | 205.04 | 73.33500 | 131.72500 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Alexander Majorov | 2018 | 237.79 | 67.77500 | 138.17500 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| Alexander Majorov | 2019 | 229.72 | 82.28333 | 134.80667 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 |
| Alexander Samarin | 2019 | 246.33 | 90.29667 | 164.94000 | Male | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| Alexandra Feigin | 2019 | 165.31 | 58.80000 | 105.40000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Alexandra Feigin | 2022 | 55.01 | 57.97000 | 99.46500 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| Alexandra Feigin | 2023 | 155.74 | 54.31000 | 100.92000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Alexei Bychenko | 2017 | 245.96 | 82.87333 | 158.67000 | Male | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Alexei Bychenko | 2018 | 258.28 | 82.85250 | 165.88750 | Male | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| Alexei Bychenko | 2019 | 216.60 | 75.77333 | 130.99333 | Male | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Alexia Paganini | 2018 | 149.66 | 55.10500 | 103.83500 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| Alexia Paganini | 2019 | 50.51 | 61.98333 | 110.98667 | Female | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Alexia Paganini | 2022 | 170.02 | 61.69000 | 111.81500 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| Alina Zagitova | 2018 | 207.72 | 74.27200 | 151.48600 | Female | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 |
| Alina Zagitova | 2019 | 237.50 | 75.65250 | 140.12500 | Female | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Alysa Liu | 2022 | 211.19 | 70.28333 | 135.84333 | Female | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| Amber Glenn | 2023 | 188.33 | 63.36333 | 123.12667 | Female | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Amy Lin | 2017 | 51.86 | 45.40000 | 79.62000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Amy Lin | 2018 | 49.31 | 51.14000 | 86.26000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Anastasia Gracheva | 2023 | 50.55 | 39.08000 | 0.00000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Anastasiia Gubanova | 2022 | 196.61 | 66.21000 | 128.36500 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| Anastasiia Gubanova | 2023 | 184.92 | 64.22000 | 122.31000 | Female | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Anastasiya Galustyan | 2017 | 153.47 | 56.41667 | 100.21333 | Female | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Anastasiya Galustyan | 2019 | 47.75 | 48.38000 | 84.25000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Andreas Nordebäck | 2023 | 223.52 | 75.98000 | 136.97000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Andrei Lazukin | 2019 | 248.74 | 72.49500 | 144.50500 | Male | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| Anete Lace | 2022 | 44.60 | 49.75000 | 0.00000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Angelina Kuchvalska | 2017 | 155.02 | 50.38000 | 91.51667 | Female | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Anita Östlund | 2018 | 48.99 | 52.59000 | 89.10000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| Anita Östlund | 2019 | 53.07 | 52.76000 | 91.90000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Anna Khnychenkova | 2017 | 46.98 | 48.93000 | 87.64000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Anna Pogorilaya | 2017 | 183.37 | 73.29250 | 140.19000 | Female | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Anne Line Gjersem | 2017 | 46.99 | 48.06000 | 80.62000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Anne Line Gjersem | 2018 | 45.25 | 48.70000 | 93.98000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Antonina Dubinina | 2018 | 41.40 | 36.69000 | 0.00000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Ashley Wagner | 2017 | 193.54 | 66.93000 | 121.98000 | Female | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| Boyang Jin | 2017 | 303.58 | 86.81000 | 176.90000 | Male | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| Boyang Jin | 2018 | 223.41 | 93.83750 | 183.47000 | Male | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
| Boyang Jin | 2019 | 262.71 | 85.85000 | 150.71000 | Male | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Boyang Jin | 2023 | 204.22 | 85.32000 | 142.15000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Bradie Tennell | 2018 | 199.89 | 65.51000 | 132.71500 | Female | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| Bradie Tennell | 2019 | 213.47 | 65.65667 | 131.92333 | Female | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Bradie Tennell | 2023 | 184.14 | 62.21000 | 110.15000 | Female | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Brendan Kerry | 2017 | 236.24 | 73.46667 | 139.38333 | Male | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Brendan Kerry | 2018 | 223.85 | 75.27333 | 143.16667 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 |
| Brendan Kerry | 2019 | 222.02 | 74.34000 | 139.69667 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 |
| Burak Demirboga | 2018 | 65.43 | 61.27000 | 105.95000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Burak Demirboga | 2019 | 60.79 | 56.95000 | 0.00000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Burak Demirboga | 2022 | 52.86 | 67.30000 | 100.73000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Burak Demirboga | 2023 | 65.73 | 64.33000 | 118.49000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Camden Pulkinen | 2022 | 271.69 | 65.52667 | 146.32000 | Male | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| Carolina Kostner | 2017 | 196.83 | 72.40000 | 138.12000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Carolina Kostner | 2018 | 208.88 | 74.69200 | 137.22000 | Female | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
| Chaeyeon Kim | 2023 | 203.51 | 71.39000 | 131.00000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Chafik Besseghier | 2017 | 230.13 | 77.95667 | 147.57333 | Male | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Chih-I Tsao | 2017 | 61.52 | 51.02000 | 118.61000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Chih-I Tsao | 2018 | 64.06 | 72.57000 | 122.64000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Conrad Orzel | 2023 | 67.65 | 74.29333 | 133.77667 | Male | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| Dabin Choi | 2017 | 191.11 | 55.32333 | 115.95000 | Female | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| Dabin Choi | 2018 | 55.30 | 61.32333 | 123.83667 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
| Daniel Grassl | 2022 | 266.66 | 87.23500 | 173.51000 | Male | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 |
| Daniel Grassl | 2023 | 244.43 | 83.17750 | 166.28000 | Male | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Daniel Samohin | 2018 | 214.01 | 71.04250 | 146.26000 | Male | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
| Daniel Samohin | 2019 | 205.28 | 81.23667 | 135.11333 | Male | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| Daša Grm | 2017 | 46.63 | 43.48000 | 0.00000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Daša Grm | 2018 | 144.51 | 47.40000 | 89.91000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Daša Grm | 2019 | 161.16 | 53.50000 | 93.79000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Daša Grm | 2022 | 147.12 | 47.85000 | 0.00000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Daša Grm | 2023 | 47.04 | 52.47000 | 90.58000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Denis Ten | 2017 | 234.31 | 89.21000 | 180.05000 | Male | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Deniss Vasiljevs | 2017 | 239.00 | 70.92333 | 149.97667 | Male | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Deniss Vasiljevs | 2018 | 254.86 | 80.89500 | 154.21250 | Male | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
| Deniss Vasiljevs | 2019 | 218.52 | 77.85333 | 134.93333 | Male | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Deniss Vasiljevs | 2022 | 243.00 | 87.59750 | 169.33000 | Male | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 |
| Deniss Vasiljevs | 2023 | 243.15 | 78.94333 | 150.51000 | Male | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| Dmitri Aliev | 2018 | 252.30 | 89.14750 | 162.01000 | Male | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
| Donovan Carrillo | 2018 | 200.76 | 59.07000 | 126.84000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Donovan Carrillo | 2019 | 54.99 | 71.16000 | 103.54000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Ekaterina Kurakova | 2022 | 186.43 | 61.08500 | 127.61750 | Female | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
| Ekaterina Kurakova | 2023 | 181.43 | 62.97333 | 122.36667 | Female | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Ekaterina Ryabova | 2019 | 179.88 | 59.95000 | 103.22000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Ekaterina Ryabova | 2022 | 188.50 | 62.37500 | 122.27750 | Female | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
| Eliska Brezinova | 2018 | 153.14 | 52.06000 | 97.63000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Eliska Brezinova | 2019 | 153.45 | 55.85000 | 110.92000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Eliska Brezinova | 2022 | 55.07 | 61.96500 | 103.36000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| Eliska Brezinova | 2023 | 47.29 | 55.89500 | 100.40500 | Female | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| Elizabet Tursynbayeva | 2017 | 191.99 | 62.28000 | 115.41333 | Female | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| Elizabet Tursynbayeva | 2018 | 186.85 | 60.38750 | 119.42250 | Female | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 |
| Elizabet Tursynbayeva | 2019 | 224.76 | 63.67000 | 127.53667 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 |
| Elzbieta Kropa | 2018 | 46.53 | 46.06000 | 87.81000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Emmi Peltonen | 2017 | 50.74 | 53.52000 | 107.05000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Emmi Peltonen | 2019 | 53.22 | 58.98000 | 105.39500 | Female | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Eunsoo Lim | 2019 | 205.57 | 65.56000 | 125.71667 | Female | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| Evgenia Medvedeva | 2017 | 233.41 | 78.22250 | 146.66750 | Female | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| Evgenia Medvedeva | 2019 | 223.80 | 64.19000 | 131.17000 | Female | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| Gabrielle Daleman | 2017 | 213.52 | 68.48000 | 123.40000 | Female | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Gabrielle Daleman | 2018 | 196.72 | 69.21000 | 116.93333 | Female | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| Georgiy Reshtenko | 2023 | 59.93 | 54.52000 | 0.00000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Graham Newberry | 2017 | 62.04 | 67.79000 | 130.27000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Graham Newberry | 2022 | 210.40 | 64.49000 | 0.00000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Graham Newberry | 2023 | 61.70 | 67.57500 | 109.95500 | Male | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Haein Lee | 2022 | 196.55 | 65.26000 | 126.35333 | Female | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| Haein Lee | 2023 | 220.94 | 66.04667 | 128.56333 | Female | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Hanul Kim | 2018 | 170.68 | 57.74000 | 116.66500 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
| Helery Hälvin | 2017 | 51.94 | 51.72000 | 94.96000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Hongyi Chen | 2019 | 157.59 | 54.44000 | 96.06000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Ihor Reznichenko | 2017 | 63.88 | 54.81000 | 0.00000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Ihor Reznichenko | 2018 | 51.70 | 63.96000 | 101.69000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Ihor Reznichenko | 2019 | 50.15 | 59.99000 | 0.00000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Ilia Malinin | 2023 | 288.44 | 83.91667 | 192.98333 | Male | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Isabeau Levito | 2023 | 207.65 | 71.03000 | 135.67000 | Female | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Isadora Williams | 2018 | 42.16 | 55.74000 | 88.44000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| Isadora Williams | 2019 | 143.22 | 47.92000 | 90.34000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Ivan Pavlov | 2017 | 69.26 | 68.94000 | 133.93000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Ivan Shmuratko | 2019 | 62.99 | 67.26000 | 111.03000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Ivan Shmuratko | 2022 | 196.65 | 80.12000 | 130.04500 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| Ivett Tóth | 2017 | 160.77 | 61.49000 | 111.16000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Ivett Tóth | 2018 | 136.87 | 51.96000 | 97.74500 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| Ivett Tóth | 2019 | 54.87 | 54.90000 | 105.93000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Janna Jyrkinen | 2023 | 160.91 | 51.83000 | 113.87500 | Female | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Jari Kessler | 2023 | 61.94 | 67.87000 | 114.96000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Jason Brown | 2017 | 269.57 | 80.28333 | 163.95000 | Male | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Jason Brown | 2019 | 254.15 | 86.48000 | 163.58333 | Male | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| Javier Fernández | 2017 | 301.19 | 96.03250 | 189.46000 | Male | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Javier Raya | 2017 | 66.88 | 66.67000 | 128.87000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Jenni Saarinen | 2022 | 55.30 | 57.95000 | 98.73000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| Jinseo Kim | 2017 | 68.66 | 64.26000 | 130.79000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Jorik Hendrickx | 2017 | 214.02 | 79.82000 | 152.82667 | Male | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Josefin Taljegård | 2022 | 163.24 | 56.37500 | 106.06000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| Joshi Helgesson | 2017 | 52.07 | 50.96667 | 98.12333 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 |
| Júlia Láng | 2023 | 44.26 | 46.33000 | 83.95000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Julia Sauter | 2019 | 53.11 | 54.29000 | 98.86000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Julia Sauter | 2023 | 165.62 | 54.48000 | 103.96000 | Female | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Julian Zhi-Jie Yee | 2017 | 213.99 | 72.21000 | 130.46000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Julian Zhi-Jie Yee | 2018 | 209.03 | 71.01500 | 129.23000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
| Julian Zhi-Jie Yee | 2019 | 205.97 | 67.70667 | 119.06667 | Male | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| Junhwan Cha | 2019 | 229.26 | 89.52000 | 164.80000 | Male | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| Junhwan Cha | 2022 | 82.43 | 97.48750 | 168.24750 | Male | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 |
| Junhwan Cha | 2023 | 296.03 | 86.18667 | 170.13000 | Male | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Kaetlyn Osmond | 2017 | 218.13 | 72.57000 | 127.19750 | Female | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 |
| Kaetlyn Osmond | 2018 | 223.23 | 75.25500 | 141.21000 | Female | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| Kailani Craine | 2017 | 152.94 | 54.70000 | 82.21000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Kailani Craine | 2018 | 154.41 | 54.17333 | 96.72000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 |
| Kailani Craine | 2019 | 48.82 | 59.42500 | 92.44500 | Female | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Kailani Craine | 2022 | 161.75 | 53.69500 | 106.56000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
| Kaori Sakamoto | 2019 | 222.83 | 68.03500 | 139.41250 | Female | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Kaori Sakamoto | 2022 | 236.09 | 75.85333 | 148.28000 | Female | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| Kaori Sakamoto | 2023 | 224.61 | 71.88333 | 132.13000 | Female | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Karen Chen | 2017 | 199.29 | 57.54667 | 117.34000 | Female | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| Karen Chen | 2022 | 192.51 | 65.84000 | 119.94000 | Female | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| Kazuki Tomono | 2018 | 256.11 | 79.88000 | 152.05000 | Male | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Kazuki Tomono | 2022 | 269.37 | 92.27333 | 167.15667 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 |
| Kazuki Tomono | 2023 | 273.41 | 87.26500 | 163.03500 | Male | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Keegan Messing | 2018 | 252.30 | 82.47000 | 153.85667 | Male | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| Keegan Messing | 2019 | 237.64 | 84.15500 | 163.24000 | Male | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 |
| Keegan Messing | 2022 | 235.03 | 90.51667 | 161.82000 | Male | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| Keegan Messing | 2023 | 265.16 | 82.17000 | 161.60000 | Male | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| Keiji Tanaka | 2017 | 222.34 | 75.72333 | 155.45333 | Male | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| Keiji Tanaka | 2018 | 236.66 | 85.97333 | 164.79667 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
| Keiji Tanaka | 2019 | 238.40 | 81.29333 | 143.60000 | Male | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Kerstin Frank | 2017 | 50.54 | 51.47000 | 80.61000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Kévin Aymoz | 2019 | 247.47 | 82.61667 | 153.24667 | Male | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| Kévin Aymoz | 2022 | 245.46 | 73.87750 | 165.90667 | Male | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| Kévin Aymoz | 2023 | 282.97 | 86.35500 | 161.95000 | Male | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Kevin Reynolds | 2017 | 253.84 | 78.46500 | 155.22000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| Kimmy Repond | 2023 | 194.09 | 63.83000 | 128.68000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Kyarha Van Tiel | 2019 | 41.85 | 44.00000 | 0.00000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Kyrylo Marsak | 2023 | 68.60 | 70.41000 | 111.57000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Lara Naki Gutmann | 2022 | 164.39 | 53.88500 | 107.39500 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 |
| Lara Naki Gutmann | 2023 | 178.43 | 55.39000 | 113.90000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Larkyn Austman | 2018 | 50.17 | 46.60500 | 81.77000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| Larry Loupolover | 2017 | 38.97 | 51.30000 | 0.00000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Larry Loupolover | 2018 | 61.82 | 52.44000 | 0.00000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Laurine Lecavelier | 2017 | 162.99 | 65.21000 | 121.16500 | Female | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Laurine Lecavelier | 2018 | 173.23 | 58.37333 | 99.92333 | Female | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| Laurine Lecavelier | 2019 | 170.59 | 58.17333 | 111.72667 | Female | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Léa Serna | 2022 | 54.30 | 62.45500 | 108.21000 | Female | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Lindsay Van Zundert | 2022 | 171.39 | 54.08000 | 116.57000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| Lindsay Van Zundert | 2023 | 159.55 | 56.15333 | 101.56333 | Female | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| Loena Hendrickx | 2017 | 172.82 | 55.41000 | 117.30000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Loena Hendrickx | 2018 | 192.31 | 55.14500 | 119.25000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| Loena Hendrickx | 2019 | 186.29 | 58.65000 | 128.05000 | Female | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Loena Hendrickx | 2022 | 217.70 | 71.07500 | 138.30000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 |
| Loena Hendrickx | 2023 | 210.42 | 72.43000 | 130.09000 | Female | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Luc Maierhofer | 2019 | 65.78 | 63.63000 | 125.37000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Lukas Britschgi | 2019 | 54.58 | 55.86000 | 0.00000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Lukas Britschgi | 2023 | 257.34 | 72.62000 | 155.14667 | Male | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| Madeline Schizas | 2022 | 188.14 | 63.65333 | 121.10000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 |
| Madeline Schizas | 2023 | 187.49 | 64.40000 | 111.65333 | Female | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| Mai Mihara | 2017 | 197.88 | 66.91333 | 126.77000 | Female | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| Mai Mihara | 2023 | 205.70 | 73.46333 | 136.45000 | Female | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Mana Kawabe | 2022 | 182.44 | 63.29000 | 122.94000 | Female | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| Maria Sotskova | 2017 | 192.20 | 69.14500 | 127.74000 | Female | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Maria Sotskova | 2018 | 196.61 | 68.09000 | 135.20800 | Female | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 |
| Mariah Bell | 2017 | 187.23 | 61.06500 | 123.28000 | Female | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Mariah Bell | 2018 | 174.40 | 61.33333 | 118.81333 | Female | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| Mariah Bell | 2019 | 208.07 | 65.44667 | 128.93667 | Female | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| Mariah Bell | 2022 | 208.66 | 65.18667 | 135.96000 | Female | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
| Marilena Kitromilis | 2022 | 53.32 | 44.03000 | 0.00000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Marilena Kitromilis | 2023 | 48.92 | 49.86000 | 97.33500 | Female | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Mark Gorodnitsky | 2023 | 232.13 | 64.94000 | 137.40000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Matteo Rizzo | 2018 | 225.44 | 76.94500 | 148.97500 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| Matteo Rizzo | 2019 | 257.66 | 78.83333 | 153.70000 | Male | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Matteo Rizzo | 2022 | 255.75 | 83.62000 | 167.66000 | Male | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
| Matteo Rizzo | 2023 | 256.04 | 82.07000 | 168.50000 | Male | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| Maurizio Zandrón | 2022 | 228.27 | 70.75000 | 123.16000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Maurizio Zandrón | 2023 | 194.31 | 70.39000 | 134.31000 | Male | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Max Aaron | 2018 | 241.49 | 81.96667 | 168.81333 | Male | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| Maxim Kovtun | 2017 | 245.84 | 77.35333 | 162.30667 | Male | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| Mia Caroline Risa Gomez | 2023 | 43.54 | 49.14000 | 88.48000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Michael Christian Martinez | 2017 | 196.79 | 72.47000 | 141.68000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Michaela Hanzlikova | 2017 | 32.21 | 52.39000 | 85.84000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Michal Březina | 2017 | 226.26 | 74.94333 | 143.29333 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 |
| Michal Březina | 2018 | 243.99 | 78.61250 | 153.57750 | Male | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 |
| Michal Březina | 2019 | 254.28 | 87.06750 | 159.68250 | Male | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Mihhail Selevko | 2023 | 230.94 | 76.29667 | 131.37667 | Male | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Mikhail Kolyada | 2017 | 257.47 | 84.14000 | 156.25000 | Male | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Mikhail Kolyada | 2018 | 272.32 | 91.64800 | 179.47000 | Male | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 1 |
| Mikhail Kolyada | 2019 | 262.44 | 83.78333 | 151.24333 | Male | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Mikhail Shaidorov | 2023 | 236.93 | 72.43000 | 164.71000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Mirai Nagasu | 2018 | 187.52 | 62.75000 | 123.66667 | Female | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
| Misha Ge | 2017 | 243.45 | 75.54667 | 155.96667 | Male | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| Misha Ge | 2018 | 249.57 | 84.15000 | 167.74250 | Male | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 |
| Morisi Kvitelashvili | 2017 | 239.24 | 76.85000 | 161.35000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Morisi Kvitelashvili | 2018 | 67.01 | 80.23750 | 146.21250 | Male | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
| Morisi Kvitelashvili | 2019 | 240.74 | 77.18667 | 147.31000 | Male | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Morisi Kvitelashvili | 2022 | 272.03 | 89.42750 | 166.00500 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 |
| Morisi Kvitelashvili | 2023 | 212.32 | 63.13000 | 132.41667 | Male | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Nam Nguyen | 2018 | 67.79 | 76.88333 | 153.27667 | Male | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| Nam Nguyen | 2019 | 237.27 | 77.21000 | 146.26333 | Male | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| Natasha Mckay | 2017 | 50.10 | 45.97000 | 94.88000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Natasha Mckay | 2018 | 45.89 | 45.12000 | 0.00000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Natasha Mckay | 2019 | 151.56 | 48.20000 | 91.88000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Natasha Mckay | 2022 | 159.27 | 54.80500 | 104.67000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| Nathan Chen | 2017 | 290.72 | 92.30250 | 188.70250 | Male | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Nathan Chen | 2018 | 321.40 | 97.56250 | 190.82000 | Male | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
| Nathan Chen | 2019 | 323.42 | 90.17000 | 188.02000 | Male | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Nicholas Vrdoljak | 2017 | 57.28 | 53.45000 | 0.00000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Nicholas Vrdoljak | 2018 | 59.74 | 58.30000 | 0.00000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Nicole Rajičová | 2017 | 165.55 | 57.44000 | 111.54667 | Female | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Nicole Rajičová | 2018 | 49.87 | 57.59750 | 111.02250 | Female | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| Nicole Rajičová | 2019 | 51.22 | 64.08000 | 104.95000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Nicole Schott | 2017 | 161.41 | 56.88000 | 103.75000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Nicole Schott | 2018 | 174.13 | 54.66500 | 112.18750 | Female | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
| Nicole Schott | 2019 | 170.56 | 50.68000 | 98.58000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Nicole Schott | 2022 | 188.42 | 60.64500 | 111.20500 | Female | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 |
| Nicole Schott | 2023 | 197.76 | 57.06000 | 111.46667 | Female | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Niina Petrõkina | 2022 | 176.60 | 58.30000 | 128.77000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Niina Petrõkina | 2023 | 193.49 | 60.51333 | 121.27667 | Female | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| Nika Egadze | 2023 | 65.17 | 79.95667 | 149.01333 | Male | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Nikita Starostin | 2022 | 205.72 | 72.12000 | 142.28000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Nikita Starostin | 2023 | 217.87 | 74.70000 | 123.27000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Nikolaj Majorov | 2022 | 216.45 | 78.54000 | 142.24000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| Nina Pinzarrone | 2023 | 191.78 | 61.35000 | 124.57000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Olga Mikutina | 2022 | 182.98 | 59.46333 | 109.63667 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
| Olga Mikutina | 2023 | 172.31 | 58.57667 | 105.56667 | Female | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Patrick Chan | 2017 | 295.16 | 90.54750 | 179.80250 | Male | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 |
| Paul Fentz | 2017 | 217.91 | 72.68000 | 153.17000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Paul Fentz | 2018 | 230.92 | 71.91667 | 131.79000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 |
| Paul Fentz | 2019 | 63.24 | 73.99000 | 141.27500 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Pernille Sørensen | 2019 | 54.36 | 50.59000 | 81.19000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Phillip Harris | 2018 | 187.69 | 67.77000 | 140.45000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Rika Hongo | 2017 | 169.83 | 62.84667 | 110.60667 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 |
| Rika Kihira | 2019 | 223.49 | 72.14750 | 149.18750 | Female | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Rinka Watanabe | 2023 | 192.81 | 64.95250 | 130.59000 | Female | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| Romain Ponsart | 2018 | 229.20 | 62.63000 | 136.79000 | Male | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Roman Sadovsky | 2022 | 245.36 | 73.43333 | 157.00000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 |
| Satoko Miyahara | 2018 | 210.08 | 71.61200 | 138.13200 | Female | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
| Satoko Miyahara | 2019 | 215.95 | 72.48667 | 141.01000 | Female | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Shoma Uno | 2017 | 319.31 | 93.71000 | 190.03250 | Male | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| Shoma Uno | 2018 | 273.77 | 100.74200 | 192.31200 | Male | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 |
| Shoma Uno | 2019 | 270.32 | 91.19750 | 188.28250 | Male | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| Shoma Uno | 2022 | 312.48 | 99.18333 | 185.42667 | Male | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| Shoma Uno | 2023 | 301.14 | 93.87667 | 191.91333 | Male | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| Shuran Yu | 2017 | 52.87 | 43.26000 | 75.14000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Sihyeong Lee | 2022 | 225.06 | 72.41000 | 144.05000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
| Slavik Hayrapetyan | 2017 | 57.14 | 60.69000 | 120.09000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Slavik Hayrapetyan | 2018 | 199.72 | 69.49000 | 127.14000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Slavik Hayrapetyan | 2019 | 60.66 | 59.87000 | 0.00000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Sofia Samodurova | 2019 | 208.58 | 68.23250 | 135.48750 | Female | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Sofja Stepchenko | 2023 | 158.38 | 55.32000 | 104.02000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Sophia Schaller | 2019 | 48.72 | 44.20000 | 0.00000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Sota Yamamoto | 2023 | 232.39 | 94.59000 | 168.77667 | Male | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Stéphane Walker | 2017 | 64.04 | 62.86000 | 133.88000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Stéphane Walker | 2018 | 65.79 | 65.96000 | 119.45000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Tomás Guarino Sabaté | 2022 | 208.95 | 66.20000 | 112.47000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Tomás Guarino Sabaté | 2023 | 67.60 | 71.65000 | 133.54000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Tzu-Han Ting | 2022 | 55.24 | 49.15000 | 96.42000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Valentina Matos | 2019 | 50.25 | 42.86000 | 0.00000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Valtter Virtanen | 2017 | 59.45 | 56.52000 | 107.57000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Valtter Virtanen | 2018 | 55.49 | 60.23000 | 121.54000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Valtter Virtanen | 2019 | 55.73 | 48.16000 | 106.58000 | Male | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Vincent Zhou | 2018 | 235.24 | 76.96000 | 174.89333 | Male | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| Vincent Zhou | 2019 | 281.16 | 84.15333 | 156.31000 | Male | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Vincent Zhou | 2022 | 277.38 | 98.47000 | 179.65500 | Male | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Viveca Lindfors | 2018 | 166.23 | 51.62000 | 96.27000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Vladimir Litvintsev | 2019 | 230.84 | 73.60000 | 130.68000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Vladimir Litvintsev | 2022 | 233.62 | 83.80500 | 158.14000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| Vladimir Samoilov | 2023 | 61.48 | 78.26000 | 113.33000 | Male | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Wakaba Higuchi | 2017 | 188.05 | 62.14333 | 121.83000 | Female | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Wakaba Higuchi | 2018 | 210.90 | 71.13000 | 136.13667 | Female | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| Wakaba Higuchi | 2022 | 188.15 | 68.93000 | 139.27667 | Female | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| Xiangning Li | 2017 | 175.37 | 55.14000 | 105.92000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| Xiangning Li | 2018 | 50.06 | 55.97750 | 107.51500 | Female | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
| Yasmine Kimiko Yamada | 2017 | 47.86 | 42.33000 | 0.00000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Yelim Kim | 2023 | 174.30 | 68.88500 | 128.39500 | Female | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Yi Christy Leung | 2019 | 177.22 | 53.93000 | 110.86000 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Young You | 2022 | 204.91 | 69.25250 | 138.80250 | Female | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
| Yuma Kagiyama | 2022 | 297.60 | 96.43000 | 195.06333 | Male | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| Yuzuru Hanyu | 2017 | 321.59 | 96.77750 | 193.75750 | Male | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| Yuzuru Hanyu | 2019 | 300.97 | 108.61000 | 179.16000 | Male | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| Zijun Li | 2017 | 159.80 | 61.86000 | 115.23333 | Female | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 |
Here are the final variables I have selected to use in my dataframe for analysis:
skater: The skater’s full name
year: the ending year of the season that the
competitions are a part of
worldscore: the total score of the skaters in the
World Championships competition
spavg: a skater’s average short program score within
that season
fpavg: a skater’s average free program score within
that season
sex: the skater’s sex. Male or Female.
The competition variables: the event name abbreviation. A 1 indicates that the skater attended the event that season, and a 0 indicates that they did not. The abbreviations are as follows:
GPUSA: Grand Prix Skate America
GPCAN: Grand Prix Skate Canada
GPFRA: Grand Prix de France
GPCHN: Grand Prix Cup of China
GPFIN: Grand Prix Espoo/Helsinki
GPJPN: Grand Prix NHK Trophy
GPGBR: Grand Prix MK John Wilson Trophy
GPITA: Grand Prix Gran Premio d’Italia
GPRUS: Gran Prix Rostelecom Cup
GPF: Grand Prix Final
EC: European Championships
4CC: 4 Continents Championships
OLY: Olympics
Let’s take a deeper look into the different variables. The first thing to note is that Men’s Singles tend to have higher score than Women’s because of the difference in difficulties between the categories. We can see that reflected in the histograms below, where we see the distribution of the Worlds Total scores between the 2 sexes. We can see their distribution between the 2 sexes differ a lot, so it is important during our analysis that we differ the 2 sexes from each other.
data %>%
ggplot(aes(x = worldscore, color = sex)) +
geom_histogram(alpha = 0.5, aes(fill = sex), position = 'identity')+
labs(title = 'Scores Between Sexes at Worlds', x = 'Total Scores')+
theme_bw()
We can see how the data clusters between those that did both the free and short program at Worlds, and those that did not qualify to the Free Program. For those that only participated in the short program, men seems to have a peak at around a score of 60, while women peak at around 50. For those that participated in both segments, we can see the bulk of the women’s score ranging from about 150 to 200, while the bulk of the men’s ranges from about 225 to 260. There is no limit to figure skating scores, but the world record in total scores for women’s is 272.71 and for men’s is 335.3, so we will expect those to be the maximum scores for the data we have.
There is also a difference in the spread of scores between years for both segments. We can see the change in distribution between years for average short program scores and average free program scores below. For the free program scores, I filtered out any missing free program scores that were filled in as 0 so that we can get a more accurate look at the differences in scoring for the free program.
data %>%
ggplot(aes(x = year, y = spavg, group = year, color = sex)) +
geom_boxplot(fill = 'grey')+
facet_wrap(~sex)+
labs(title = 'Distribution of Avg. SP Score Between Years and Sexes', y = 'SP Score Averages', x = 'Year')+
theme_bw()
data %>%
filter(fpavg != 0) %>%
ggplot(aes(x = year, y = fpavg, group = year, color = sex)) +
geom_boxplot(fill = 'grey')+
facet_wrap(~sex)+
labs(title = 'Distribution of Avg. FS Score Between Years and Sexes', y = 'FS Score Averages', x = 'Year')+
theme_bw()
Figure skating has a lot of rule changes throughout the years, and these rule changes affect how a skater is able to earn points. Sometimes it might be easier for them to earn a few extra points or sometimes it could be harder to get an extra point boost over other skaters. These changes can be reflected in how the distribution of scores vary throughout the years, and it is why we should look at how the year affects a skater’s score. We can also see that average free program score varies more throughout the years compared to short program scores. The short programs have more rigorous rules that restricts a skater’s score-earning potential and the free program is when it is more open to point-earning but also the most affected by rule changes.
Different competitions have different amounts of competitors and ways to qualify for them. The 6 Grand Prix events are invite-only, usually allowing 10 to 12 competitors for each for Men’s and Women’s. A skater can be invited to 1 or 2 of these events. On top of those 6 events, the Grand Prix Final has the top 6 skaters for each gender in the GP events compete against each other. The 4 Continents, European Championships, and Olympics allow each ISU-recognized nation to have at least 1 skater enter the competition. A nation could earn up to 3 spots based on previous performances at other events. A skater also needs to have a minimum technical score (a component of their score for a program) to enter. Because of all of these rules, these 3 events can have varying amounts of competitors in them. We can see this broken down below with the number of competitors in each competition for the 2022 season.
reg_merged %>%
filter(year == 2022) %>%
ggplot(aes(y= competition))+
geom_bar(aes(fill = sex))+
facet_wrap(~sex)+
labs(title = 'Number of Competitors')+
theme_bw()
As mentioned before, there is a discrepancy with scoring between different competitions. Let’s take a look at the scores from the 2022-2023 season so we can see how the spread and median of scores differ.
reg_merged %>%
filter(year == 2023) %>%
ggplot(aes(x = competition, y = total_score, color = sex)) +
geom_boxplot(fill = 'grey')+
geom_jitter(alpha = 0.4)+
facet_wrap(~sex)+
labs(title = 'Distribution of Score across 2022-23 Competitions', x = 'Competition', y = 'Total Score')+
theme_bw()+
theme(axis.text.x=element_text(angle=90,hjust=1))
All the competitions have differing medians and ranges of scores. A popular fan opinion for this season was that GP Japan was relatively easy on the scoring, so a lot of mistakes weren’t called to deduct from a skater’s points. This can be reflected with a higher median score compared to the other GP events across both sexes.
It is also important to understand that average total score is not a good predictor of a skater’s score at the World Championship. The graphs below show World Score on the x-axis and average Total Score on the y-axis by year and sex. If average Total Score was the sole predictor of World Score, we would see the graphs with only positive sloping lines; however, that is not the case. Someone with a higher average score could score lower than someone with a lower average score. That is why we need to look at other variables that could affect a skater’s scores throughout the season and at Worlds.
graphdata %>%
ggplot(aes(x = worldscore)) +
geom_line(aes(y=tsavg, color = sex))+
labs(x = 'World Score', y = 'Average Total Score', title = 'Average Total Score vs. World Score')+
facet_wrap(~year)+
theme_bw()
Our first step before fitting any models is to split our data into 2
sets, the training and testing set. The training set we will use to fit
our model onto and the testing set is used to see how well our models
will perform with unseen data. I chose a 70/30 split, where 70% of the
data will go into the training set and 30% into the testing set. I also
stratified the split on the response variable, worldscore,
so that we get an even distribution of the variable in both sets, which
is especially useful since we have that big gap in scores between the
24th and 25th place at Worlds, as mentioned before.
set.seed(1012) # for reproducibility
# 70/30 split, stratify on world_rank
scores_split <- initial_split(data, prop = 0.7, strata = worldscore)
score_train <- training(scores_split)
score_test <- testing(scores_split)
Let’s check the dimensions for the training dataset:
dim(score_train)
[1] 223 19
And the testing dataset:
dim(score_test)
[1] 96 19
The training set has 223 observations while the testing set has 96. This looks about right for our 70/30 split.
Now we will build a recipe with our predictor and response variables
to use for the models we will create. We can make one universal recipe,
since we will use the same predictors and response for each model. We
want to use all of the variables in our dataset, except for the skater’s
name, and have worldscore be our response variable. We also
want to make sure we turn any nominal variables into dummy variables,
which indicates the presence or absence of a certain level of the
nominal variable with either a 0 or 1. Lastly, we will also normalize
our data, since the ranges of spavg and fpavg
vary. Below is our recipe:
# Create Recipe
score_recipe <- recipe(worldscore ~ ., data = score_train) %>%
step_rm('skater') %>%
step_dummy(all_nominal_predictors()) %>%
step_normalize()
Next, we will prepare our data for k-fold cross validation. K-fold cross validation will split our training data into k subset or folds. One fold will be set aside, while the models we create will be fit on to the remaining k-1 folds. Then the fitted models will be tested on the fold that was set aside. We will repeat these steps k times, making sure to have a different fold be set aside each time. In the end, we will take the average of the model performance metrics from the testing fold to see how well the model performs on the training data.
K-fold cross validation allows a more accurate evaluation of a model’s performance since we are fitting and evaluating the model on multiple different splits. We measure multiple performance metrics and take an average rather than just measuring one metric.
For our analysis, we will use 10 folds. Below, we are splitting our
training data into 10 folds while also stratifying on our response
variable, worldscore, to make sure there is a balanced
distribution of the variable between the different folds.
score_folds <- vfold_cv(score_train, v = 10, strata = worldscore)
Now we will build our model and fit them onto the data. I will be fitting 5 different types of models: K-nearest neighbors, linear regression, elastic net, random forest, and gradient-boosted tree. For each model, we will be following these steps:
Set up the model, tuning for any parameters that are needed. Add the engine and specify regression mode if needed.
Set up the workflow, adding the model and recipe.
Create tuning grid if there are any tuning parameters. Specify the range and levels of these parameters.
Tune the model with the workflow, cross-validation flows, and the tuning grid.
Save the tuned models to a RDS file and later, load back in the saved files. This will help us save time from continuously rerunning any time-consuming models.
K-nearest neighbors is a non-parametric model that measures the distance between data points to make predictions. It selects k nearest data points to the new data point we are trying to predict and averages the data points to predict the outcome of the new data. To choose k, we will tune it and choose the best performing k from a range of 1 to 10 with 10 levels.
##K-nearest neighbors
#set up model, tuning for neighbors parameter
knn_model <- nearest_neighbor(neighbors = tune()) %>%
set_mode('regression') %>%
set_engine('kknn')
#set up workflow and add model and recipe
knn_workflow <- workflow() %>%
add_model(knn_model) %>%
add_recipe(score_recipe)
#set up grid
knn_grid <- grid_regular(neighbors(range = c(1, 10)), levels = 10)
#tune model
knn_tune <- tune_grid(
object = knn_workflow,
resamples = score_folds,
grid = knn_grid
)
#save models
write_rds(knn_tune, file = 'tuned_models/knn.rds')
Linear regression is one of the most simple models, creating a linear relationship between the response variable and the predictor variables. It is not very flexible but it is easy to use and understand. There are no parameters to tune, so after setting up our workflow, we can skip right into fitting the model onto the folds.
##Linear regression
#set up model
lm_model <- linear_reg() %>%
set_engine('lm')
#set up workflow
lm_workflow <- workflow() %>%
add_model(lm_model) %>%
add_recipe(score_recipe)
#no tuning for linear regression, so we will skip into fitting the model onto the cross validation folds
lm <- fit_resamples(lm_workflow, resamples = score_folds)
Elastic Net combines the regularization techniques of Lasso
regression (shrinking irrelevant variables to 0) and Ridge regression
(shrinking some variables to smaller numbers but not 0). This will help
us get rid of irrelevant variables and also fix any multicollinearity
problems. The model has 2 parameters: mixture which tells
us what proportion of Lasso regularization is used in the model (0 for
only Ridge regression, 1 for only Lasso), and penalty which
tells us the overall level of regularization in the model. We will tune
these 2 parameters on a range of 0 to 1 for mixture and 0
to 3 for penalty, each with 10 levels.
##Elastic net
#set up model, tuning for penalty and mixture
en_model <- linear_reg(mixture = tune(), penalty = tune()) %>%
set_engine("glmnet") %>%
set_mode("regression")
#set up workflow
en_workflow <- workflow() %>%
add_model(en_model) %>%
add_recipe(score_recipe)
#set up grid
en_grid <- grid_regular(penalty(range = c(0,3), trans = identity_trans()), mixture(range = c(0,1)), levels = 10)
#tune model
en_tune <- tune_grid(
object = en_workflow,
resamples = score_folds,
grid = en_grid
)
#save model
write_rds(en_tune, file = 'tuned_models/en.rds')
A Random Forest model creates multiple decision trees that are each
trained on a random subset of training data with a random subset of
predictors. The predictions of all the trees are averaged to make
predictions on new data. The model was 3 parameters: mtry
which tells us the number of predictors that are randomly sampled at
each split, trees which tells us the number of trees we
want to create in the Random Forest, and min_n which tells
us the minimum number of observations in the data sample that is
required for the tree to continue splitting. We are tuning these
parameters, with mtry allowed a range of 1 to 17 (the total
number of predictors we have), trees gets a range of 200 to
500, and min_n gets a range of 8 to 13.
## Random Forest
#set up model, tuning for mtry, trees, min_n
rf_model <- rand_forest(mtry = tune(),
trees = tune(),
min_n = tune()) %>%
set_engine('ranger', importance = 'impurity') %>%
set_mode('regression')
#set up workflow
rf_flow <- workflow() %>%
add_model(rf_model) %>%
add_recipe(score_recipe)
#set up grid
#rf_grid <- grid_regular(mtry(range = c(1, 17)),
#trees(range = c(200, 500)),
#min_n(range = c(8, 13)), levels = 10)
#tune model
#rf_tune <- tune_grid(
#object = rf_flow,
#resamples = score_folds,
#grid = rf_grid)
#save model
#write_rds(rf_tune, file = 'tuned_models/rf.rds')
Gradient-Boosted Trees sequentially build trees based on the
residuals of a model, rather than the outcome variable. It adds new
trees to update the residuals of the previous trees so that it
eventually minimizes overall error. We will again tune the parameters
mtry and trees with the same range, but this
time we will tune learn_rate rather than min_n
since it has a bigger impact on the performance of the model. We gave
learn_rate a range of 0.01 to 0.1 with 10 levels for each
of the parameters, which tells us the rate that the boosted tree will
learn.
##Gradient-boosted tree
#set up model, tuning for mtry, tress, learn_rate
gbt_model <- boost_tree(mtry = tune(),
trees = tune(),
learn_rate = tune()) %>%
set_engine('xgboost') %>%
set_mode('regression')
#set up workflow
gbt_flow <- workflow() %>%
add_model(gbt_model) %>%
add_recipe(score_recipe)
#set up grid
gbt_grid <- grid_regular(mtry(range = c(1, 17)),
trees(range = c(200, 500)),
learn_rate(range = c(0.01, 0.1)),
levels = 10)
#tune model
gbt_tune <- tune_grid(
object = gbt_flow,
resamples = score_folds,
grid = gbt_grid
)
#save model
write_rds(gbt_tune, file = 'tuned_models/gbt.rds')
With all of our models competed and saved, lets load back in the saved results and analyze their performance.
#K Nearest Neighbors
knn <- read_rds(file = 'tuned_models/knn.rds')
#Linear model saved under lm variable
#Elastic Net
en <- read_rds(file = 'tuned_models/en.rds')
#Random Forest
rf <- read_rds(file = 'tuned_models/rf.rds')
#Boosted Trees
bt <- read_rds(file = 'tuned_models/gbt.rds')
To analyze the performances of the models, I will use Root Mean Square Error (RMSE) as my performance metric. RMSE is a common measure of a regression model’s performance, showing us how far away the model’s predicted value is from the data’s true value. A better performing model will have a lower RMSE, telling us that the predicted values are closer to the true values. The RMSE value we will be comparing is the average RMSE across the our cross-validation folds. We will begin by comparing the models we tuned and see which values for the parameters we tuned worked the best based on RMSE.
Let’s take a look at which K-Nearest Neighbors model performed the best. Below, we can see a graph of the RMSE for each k value. The best performing k value is the one with the lowest RMSE, which looks to be k = 10
autoplot(knn, metric = 'rmse')
Next, let’s take a look at the RMSE for the Elastic Net model. The x-axis shows the penalty, the different colored lines show the mixture, and the y-axis shows the RMSE. In general, when the penalty and mixture is higher, the RMSE is lower and the model performs better.
autoplot(en, metric = 'rmse')
Now, let’s look at the RMSE for the Random Forest model. The x-axis shows the mtry, the different colored lines show the number of trees, and the y-axis shows the RMSE. In general, when the value for mtry was higher, the RMSE is lower and the model performs better.
autoplot(rf, metric = 'rmse')
Finally, let’s take a look at the RMSE for the Gradient-Boosted Tree model. The x-axis shows the mtry, the different colored lines show the number of trees, the different graphs show the learning rate, and the y-axis shows the RMSE. Across the parameters, there doesn’t seem to be a pattern of which level of the parameters performed the best.
autoplot(bt, metric = 'rmse')
Now that we get a general sense of which parameters worked best,
let’s compare the different types of models with each other. We will use
the function show_best to choose the best model based on
the lowest RMSE value. Below are the tables showing the best performing
models for each type. The variable mean is the mean RMSE
value across the cross-validation folds, and the variable
std_err is the standard deviation of the RMSE values.
#K-nearest Neighbors
knn_best <- show_best(knn, metric = 'rmse', n =1)
knn_best %>%
kable(caption = 'Best K-nearest Neighbors Model') %>%
kable_styling(full_width = F, position = "left")
| neighbors | .metric | .estimator | mean | n | std_err | .config |
|---|---|---|---|---|---|---|
| 10 | rmse | standard | 61.95858 | 10 | 1.856955 | Preprocessor1_Model10 |
#Linear Regression
lm_best <- collect_metrics(lm)[1,]
lm_best %>%
kable(caption = 'Linear Model') %>%
kable_styling(full_width = F, position = "left")
| .metric | .estimator | mean | n | std_err | .config |
|---|---|---|---|---|---|
| rmse | standard | 51.75774 | 10 | 2.882907 | Preprocessor1_Model1 |
#Elastic Net
en_best <- show_best(en, metric = 'rmse', n =1)
en_best %>%
kable(caption = 'Best Elastic Net Model') %>%
kable_styling(full_width = F, position = "left")
| penalty | mixture | .metric | .estimator | mean | n | std_err | .config |
|---|---|---|---|---|---|---|---|
| 3 | 1 | rmse | standard | 52.52107 | 10 | 1.912929 | Preprocessor1_Model100 |
#Random Forest
rf_best <- show_best(rf, metric = 'rmse', n =1)
rf_best %>%
kable(caption = 'Best Random Forest Model') %>%
kable_styling(full_width = F, position = "left")
| mtry | trees | min_n | .metric | .estimator | mean | n | std_err | .config |
|---|---|---|---|---|---|---|---|---|
| 8 | 266 | 13 | rmse | standard | 49.63054 | 10 | 2.311282 | Preprocessor1_Model525 |
#Boosted Tree
bt_best <- show_best(bt, metric = 'rmse', n =1)
bt_best %>%
kable(caption = 'Best Gradient-Boosted Tree Model') %>%
kable_styling(full_width = F, position = "left")
| mtry | trees | learn_rate | .metric | .estimator | mean | n | std_err | .config |
|---|---|---|---|---|---|---|---|---|
| 11 | 500 | 1.096478 | rmse | standard | 59.77649 | 10 | 2.639742 | Preprocessor1_Model0640 |
We can also compare the other models visually by plotting their mean RMSE values and the standard deviation.The graph below for the mean RMSE and the standard deviation for the models on the y-axis and the different models on the x-axis.
#combine RMSE and standard deviation for each model into a dataframe
rmse <- rbind(knn_best %>% select(mean, std_err) %>% mutate(model = 'KNN'),
en_best%>% select(mean, std_err)%>% mutate(model = 'Elastic Net'),
rf_best%>% select(mean, std_err)%>% mutate(model = 'Random Forest'),
bt_best%>% select(mean, std_err)%>% mutate(model = 'Boosted Tree'),
lm_best%>% select(mean, std_err)%>% mutate(model = 'Linear Reg.'))
rmse %>%
ggplot(aes(x = model, y = mean, ymin = mean - std_err, ymax = mean + std_err)) +
geom_pointrange()+
labs(title = 'RMSE and Standard Deviation for Each Model', x = 'Model', y = 'RMSE')+
theme_bw()
The model with that performs the best is the Random Forest model, with the lowest mean RMSE and the lowest standard deviation. The Linear Regression model performs the next best, then Elastic Net, then the Gradient-Boosted Tree model, and finally the K-Nearest Neighbors model.
Here is the best performing model with the values of the parameters.
It is a Random Forest model with a mtry value of 8, 266
trees, and a min_n value of 13. The RMSE value
for this model is about 49.63.
rf_best%>%
kable(caption = 'Best Random Forest Model') %>%
kable_styling(full_width = F, position = "left")
| mtry | trees | min_n | .metric | .estimator | mean | n | std_err | .config |
|---|---|---|---|---|---|---|---|---|
| 8 | 266 | 13 | rmse | standard | 49.63054 | 10 | 2.311282 | Preprocessor1_Model525 |
We will now finalize the our workflow with the best model and fit it
onto the training set. Below is a graph that shows which predictor
variables are the most important to predicting worldscore
in the Random Forest model. The higher the value of Importance, the more
important it is to the model. We can see that fpavg is the
most important variable, followed by spavg, and then if
skater is male.
final_flow <- finalize_workflow(rf_flow, rf_best)
final_model <- fit(final_flow, data = score_train)
final_model %>% extract_fit_parsnip() %>%
vip() +
theme_bw()
Now, we will fit the model onto the testing set and see how well the model performs on unseen data. Below is the RMSE value for the model on the testing set. The RMSE value is about 46.6, which is a better value than the RMSE from our training set. So, we can say that our model is performing well on the testing set.
final_test <- augment(final_model, new_data = score_test)
final_test%>%
rmse(truth = worldscore, estimate = .pred)
# A tibble: 1 × 3
.metric .estimator .estimate
<chr> <chr> <dbl>
1 rmse standard 46.6
We can also see a plot of the predicted values versus the real values below. The points are relatively close to the line, so our model is performing pretty well. We can also see 2 clusters of points, where the skater did not reach the free skate and has a lower score and where the skater finished both segments for a higher total score. For only short program scores, we can see that the model tends to overestimate them. On the other hand, scores that reflected 2 segments tend to be underestimated.
final_test %>%
ggplot(aes(x = .pred, y = worldscore))+
geom_point()+
geom_abline()+
theme_bw()
Something interesting would be to see how the rankings on the competition would change based on the predicted scores. Below is a slopegraph that shows the rankings of the skaters at the 2023 World Championships based on their predicted scores versus their actual rankings from their actual scores.
Below is the women’s change in ranks. The rankings do change quite a bit, but we did get the same skaters in the top 5 rankings.
compare <- augment(final_model, new_data = data)
f_23 <- compare %>%
filter(sex == 'Female', year == 2023) %>%
select(skater, .pred)
f_23 <- f_23[order(f_23$.pred, decreasing = TRUE),] %>%
mutate(predrank = 1:nrow(.))
f23compare <- merge(worldmerged %>% filter(year == 2023), f_23, by = c('skater'))
f23compare <- f23compare%>%
mutate(worldrank = ifelse(worldrank > 24, worldrank -5, worldrank)) %>%
pivot_longer(cols = c(worldrank, predrank), names_to = 'type', values_to = 'rank')
newggslopegraph(dataframe = f23compare,
Times = type,
Measurement = rank,
Grouping = skater,
ReverseYAxis = TRUE,
Title = '2023 Figure Skating Championships - Womens',
SubTitle = 'Rank Based on Predicted Vs. Actual Scores',
Caption = '')
Below is the graph for the men’s. The men’s is more accurate as we got 6 rankings correct and also have the same skater’s in the top 5.
m_23 <- compare %>%
filter(sex == 'Male', year == 2023) %>%
select(skater, .pred)
m_23 <- m_23[order(m_23$.pred, decreasing = TRUE),] %>%
mutate(predrank = 1:nrow(.))
m23compare <- merge(worldmerged %>% filter(year == 2023), m_23, by = c('skater'))
m23compare <- m23compare%>%
mutate(worldrank = ifelse(worldrank > 4, worldrank - 1 , worldrank))%>%
mutate(worldrank = ifelse(worldrank > 10, worldrank -1, worldrank)) %>%
mutate(worldrank = ifelse(worldrank > 18, worldrank -1, worldrank)) %>%
mutate(worldrank = ifelse(worldrank > 26, worldrank -1, worldrank)) %>%
pivot_longer(cols = c(worldrank, predrank), names_to = 'type', values_to = 'rank')
newggslopegraph(dataframe = m23compare,
Times = type,
Measurement = rank,
Grouping = skater,
ReverseYAxis = TRUE,
Title = '2023 Figure Skating Championships - Mens',
SubTitle = 'Rank Based on Predicted Vs. Actual Scores',
Caption = '')
We can also use our model to predict the scores for the 2024 World Championships. We will use the Random Forest model and use data from the regular competitions throughout the 2023-2024 season to predict the scores at 2024 Worlds. I got the list of skater’s entered from the ISU website and web-scrapped it into an Excel sheet. I did the same process as the other dataset to clean up the data. Since the model does not hold any data for the year of 2024, I put the year for the 2024 data as 2023. Some skaters also did not have any competition data this season, so I filled it in with any scores they had from previous seasons. If they didn’t have any data at all, I just gave them 0’s for scores. Below are the predictions for the Men’s and Women’s events:
#Read in 2024 Entries
entries <- read_xlsx("2024 entires.xlsx", sheet = '2024 Entries')
entries <- entries %>%
filter(is.na(Sub)) %>%
clean_names() %>%
select(skater) %>%
mutate(skater = str_to_title(skater)) %>%
mutate(year = as.factor(2023))
#Clean up data for scores in 2024
score24 <- clean_names(score24)
score24$skater <- gsub('[0-9]', '', score24$skater)
score24$skater <- str_squish(score24$skater)
#get average scores for 2024
avg2 <- score24 %>%
group_by(skater) %>%
mutate(spavg = mean(sp_score,na.rm = TRUE)) %>%
mutate(fpavg = mean(fs_score, na.rm=TRUE)) %>%
select(skater, competition, spavg, fpavg, sex)
#pivot table to match original dataset
avg2$truth <- 1
pivoted2 <- avg2 %>%
pivot_wider(names_from = competition, values_from = truth)
pivoted2[is.na(pivoted2)] <- 0
#merge together
worlds24 <- left_join(entries, pivoted2, by = c('skater'))
#fill in 0 for missing competition variables
worlds24$GPITA <- as.factor(0)
worlds24$GPRUS <- as.factor(0)
worlds24$GPGBR <- as.factor(0)
worlds24$OLY <- as.factor(0)
#factor competition variables
for (i in 6:18) {
if (is.numeric(worlds24[[i]])) { # Check if the column is numeric
worlds24[is.na(worlds24[[i]]), i] <- 0 # Replace NA values with 0
worlds24[[i]] <- factor(worlds24[[i]]) # Convert column to factor
}
}
#fill in missing sex variables
worlds24[c(7,21,22),5] <- 'Female'
worlds24[c(36,46, 65,73),5] <- 'Male'
#fill in missing score variables with latest scores or 0
for (i in c(7, 21, 22, 36,46, 65,73)) {
if (i == 7) {
worlds24[7, c(3,4)] <- data[93, c(4,5)]
} else if (i == 46) {
worlds24[46, c(3,4)] <- data[299, c(4,5)]
} else if (i == 73) {
worlds24[73, c(3,4)] <- data[130, c(4,5)]
} else {
worlds24[i, c(3,4)] <- 0
}
}
#predictions
table <- augment(final_model, new_data = worlds24)
#female table
female <- table %>%
filter(sex == 'Female') %>%
select(skater, .pred)
female[order(female$.pred, decreasing = TRUE),] %>%
mutate(rank = 1:nrow(.)) %>%
kable(caption = '2024 Predictions - Women') %>%
kable_styling(full_width = F, position = "left")%>%
scroll_box(width = "100%", height = "200px")
| skater | .pred | rank |
|---|---|---|
| Kaori Sakamoto | 218.92409 | 1 |
| Loena Hendrickx | 210.59419 | 2 |
| Isabeau Levito | 208.09762 | 3 |
| Chaeyeon Kim | 192.58412 | 4 |
| Hana Yoshida | 187.45573 | 5 |
| Nina Pinzarrone | 187.36935 | 6 |
| Livia Kaiser | 181.09405 | 7 |
| Mone Chiba | 179.71017 | 8 |
| Amber Glenn | 179.55052 | 9 |
| Anastasiia Gubanova | 177.64526 | 10 |
| Niina Petrõkina | 174.87683 | 11 |
| Sarina Joos | 172.70468 | 12 |
| Haein Lee | 171.98760 | 13 |
| Kimmy Repond | 166.77064 | 14 |
| Ekaterina Kurakova | 166.13705 | 15 |
| Tzu-Han Ting | 163.24541 | 16 |
| Lorine Schild | 163.17047 | 17 |
| Madeline Schizas | 160.49636 | 18 |
| Julia Sauter | 146.85595 | 19 |
| Josefin Taljegård | 143.56948 | 20 |
| Nataly Langerbaur | 142.89156 | 21 |
| Eliska Brezinova | 141.67570 | 22 |
| Kristina Isaev | 139.64798 | 23 |
| Young You | 135.95691 | 24 |
| Sofja Stepchenko | 131.24583 | 25 |
| Olga Mikutina | 128.59074 | 26 |
| Nina Povey | 125.92183 | 27 |
| Mariia Seniuk | 125.64937 | 28 |
| Alexandra Feigin | 122.10273 | 29 |
| Nella Pelkonen | 121.81301 | 30 |
| Anastasia Gozhva | 104.83027 | 31 |
| Meda Variakojyte | 66.13431 | 32 |
| Anastasia Gracheva | 66.13431 | 33 |
| Mia Caroline Risa Gomez | 61.64168 | 34 |
| Vanesa Selmeková | 56.76028 | 35 |
#male table
male <- table %>%
filter(sex == 'Male') %>%
select(skater, .pred)
male[order(male$.pred, decreasing = TRUE),] %>%
mutate(rank = 1:nrow(.)) %>%
kable(caption = '2024 Predictions - Men') %>%
kable_styling(full_width = F, position = "left")%>%
scroll_box(width = "100%", height = "200px")
| skater | .pred | rank |
|---|---|---|
| Ilia Malinin | 293.94883 | 1 |
| Yuma Kagiyama | 286.89809 | 2 |
| Shoma Uno | 280.90844 | 3 |
| Adam Siao Him Fa | 278.80246 | 4 |
| Kao Miura | 273.11882 | 5 |
| Jason Brown | 253.04533 | 6 |
| Gabriele Frangipani | 252.82965 | 7 |
| Mikhail Shaidorov | 250.89532 | 8 |
| Aleksandr Selevko | 250.82248 | 9 |
| Nika Egadze | 250.55149 | 10 |
| Lukas Britschgi | 249.62953 | 11 |
| Boyang Jin | 244.90687 | 12 |
| Junhwan Cha | 242.84779 | 13 |
| Georgiy Reshtenko | 228.41120 | 14 |
| Vladimir Litvintsev | 227.96176 | 15 |
| Vladimir Samoilov | 226.46857 | 16 |
| Mark Gorodnitsky | 216.60233 | 17 |
| Roman Sadovsky | 213.09935 | 18 |
| Camden Pulkinen | 212.89196 | 19 |
| Nikolaj Memola | 211.51361 | 20 |
| Adam Hagara | 208.12472 | 21 |
| Wesley Chiu | 204.16965 | 22 |
| Sihyeong Lee | 196.53445 | 23 |
| Nikita Starostin | 194.85654 | 24 |
| Luc Economides | 191.14220 | 25 |
| Andreas Nordebäck | 190.70275 | 26 |
| Deniss Vasiljevs | 190.16392 | 27 |
| Ivan Shmuratko | 179.30869 | 28 |
| Donovan Carrillo | 177.36139 | 29 |
| Tomás Guarino Sabaté | 176.09272 | 30 |
| Maurizio Zandrón | 173.39120 | 31 |
| Aleksandr Vlasenko | 128.38718 | 32 |
| Davidé Lewton Brain | 120.96139 | 33 |
| Valtter Virtanen | 94.97995 | 34 |
| Burak Demirboga | 90.69672 | 35 |
| Edward Appleby | 89.69571 | 36 |
| Jari Kessler | 88.47896 | 37 |
| Alexander Zlatkov | 82.61716 | 38 |
| Semen Daniliants | 68.31597 | 39 |
| Hyungyeom Kim | 68.31597 | 40 |
After fitting different models onto our training data with cross validation, we found that a Random Forest model was best at predicting the scores of skaters at the World Championships. This makes since typically Random Forests are flexible models that have higher predictive power. However, there is definitely room to improvement. We found that there was overestimation for scores that only came from the short program while underestimation from scores that came from both segments.
The worst performing was the K-Nearest Neighbors model. While it is easy to use and understand, the model does not perform well when there are a lot of variables and is sensitive to outliers, imbalanced data and overfitting. Since we have a lot of predictor variables and our score variables can have an imbalanced distribution, it makes sense that K-Nearest Neighbors did not do too well.
For the future, it would be interesting to do this prediction again but with variables that could describe the physical state of a skater or take into account the previous season’s scores. For example, in our 2024 Women’s predictions, we predicted Young You to be 24th place, but she has been making a comeback after a bad injury and after seeing her most recent domestic competition, I would predict she could be top 10. Since we also had that discrepancy in scores because of the limit of skaters that are allowed in the free skate, it would also be interesting to add a component that showed how many competitors there are or something to weigh how likely they are to get into the free skate. The overestimation for the one-segment scores come from we have data where a skater’s free skate score can be really high. But then suddenly for Worlds, their free skate score is 0. More competitors could also make it harder to make it to the free skate. It would be interesting to find a way to account for this to make the model more accurate in the score predictions.
SkatingScores, skatingscores.com/. Accessed 17 Mar. 2024.
“Figure Skating - International Skating Union.” International Skating Union, https://www.isu.org/figure-skating. Accessed 17 Mar. 2024.